diff --git a/docs/doxygen/ie_docs.xml b/docs/doxygen/ie_docs.xml
index 503f9f38986..c12cc4d22be 100644
--- a/docs/doxygen/ie_docs.xml
+++ b/docs/doxygen/ie_docs.xml
@@ -194,6 +194,7 @@ limitations under the License.
+
diff --git a/docs/ops/sort/MulticlassNMS_8.md b/docs/ops/sort/MulticlassNMS_8.md
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@@ -0,0 +1,161 @@
+## MulticlassNonMaxSuppression {#openvino_docs_ops_sort_MulticlassNonMaxSuppression_8}
+
+**Versioned name**: *MulticlassNonMaxSuppression-8*
+
+**Category**: *Sorting and maximization*
+
+**Short description**: *MulticlassNonMaxSuppression* performs multi-class non-maximum suppression of the boxes with predicted scores.
+
+**Detailed description**: *MulticlassNonMaxSuppression* is a multi-phase operation. It implements non-maximum suppression algorithm as described below:
+
+1. Let `B = [b_0,...,b_n]` be the list of initial detection boxes, `S = [s_0,...,s_N]` be the list of corresponding scores.
+2. Let `D = []` be an initial collection of resulting boxes. Let `adaptive_threshold = iou_threshold`.
+3. If `B` is empty, go to step 9.
+4. Take the box with highest score. Suppose that it is the box `b` with the score `s`.
+5. Delete `b` from `B`.
+6. If the score `s` is greater than or equal to `score_threshold`, add `b` to `D`, else go to step 9.
+7. If `nms_eta < 1` and `adaptive_threshold > 0.5`, update `adaptive_threshold *= nms_eta`.
+8. For each input box `b_i` from `B` and the corresponding score `s_i`, set `s_i = 0` when `iou(b, b_i) > adaptive_threshold`, and go to step 3.
+9. Return `D`, a collection of the corresponding scores `S`, and the number of elements in `D`.
+
+This algorithm is applied independently to each class of each batch element. The operation feeds at most `nms_top_k` scoring candidate boxes to this algorithm.
+The total number of output boxes of each batch element must not exceed `keep_top_k`.
+Boxes of `background_class` are skipped and thus eliminated.
+
+**Attributes**:
+
+* *sort_result*
+
+ * **Description**: *sort_result* specifies the order of output elements.
+ * **Range of values**: `class`, `score`, `none`
+ * *class* - sort selected boxes by class id (ascending).
+ * *score* - sort selected boxes by score (descending).
+ * *none* - do not guarantee the order.
+ * **Type**: `string`
+ * **Default value**: `none`
+ * **Required**: *No*
+
+* *sort_result_across_batch*
+
+ * **Description**: *sort_result_across_batch* is a flag that specifies whenever it is necessary to sort selected boxes across batches or not.
+ * **Range of values**: true or false
+ * *true* - sort selected boxes across batches.
+ * *false* - do not sort selected boxes across batches (boxes are sorted per batch element).
+ * **Type**: boolean
+ * **Default value**: false
+ * **Required**: *No*
+
+* *output_type*
+
+ * **Description**: the tensor type of outputs `selected_indices` and `valid_outputs`.
+ * **Range of values**: `i64` or `i32`
+ * **Type**: `string`
+ * **Default value**: `i64`
+ * **Required**: *No*
+
+* *iou_threshold*
+
+ * **Description**: intersection over union threshold.
+ * **Range of values**: a floating-point number
+ * **Type**: `float`
+ * **Default value**: `0`
+ * **Required**: *No*
+
+* *score_threshold*
+
+ * **Description**: minimum score to consider box for the processing.
+ * **Range of values**: a floating-point number
+ * **Type**: `float`
+ * **Default value**: `0`
+ * **Required**: *No*
+
+* *nms_top_k*
+
+ * **Description**: maximum number of boxes to be selected per class.
+ * **Range of values**: an integer
+ * **Type**: `int`
+ * **Default value**: `-1` meaning to keep all boxes
+ * **Required**: *No*
+
+* *keep_top_k*
+
+ * **Description**: maximum number of boxes to be selected per batch element.
+ * **Range of values**: an integer
+ * **Type**: `int`
+ * **Default value**: `-1` meaning to keep all boxes
+ * **Required**: *No*
+
+* *background_class*
+
+ * **Description**: the background class id.
+ * **Range of values**: an integer
+ * **Type**: `int`
+ * **Default value**: `-1` meaning to keep all classes.
+ * **Required**: *No*
+
+* *nms_eta*
+
+ * **Description**: eta parameter for adaptive NMS.
+ * **Range of values**: a floating-point number in close range `[0, 1.0]`.
+ * **Type**: `float`
+ * **Default value**: `1.0`
+ * **Required**: *No*
+
+**Inputs**:
+
+* **1**: `boxes` - tensor of type *T* and shape `[num_batches, num_boxes, 4]` with box coordinates. The box coordinates are layout as `[xmin, ymin, xmax, ymax]`. **Required.**
+
+* **2**: `scores` - tensor of type *T* and shape `[num_batches, num_classes, num_boxes]` with box scores. **Required.**
+
+**Outputs**:
+
+* **1**: `selected_outputs` - tensor of type *T_THRESHOLDS* and shape `[number of selected boxes, 6]` containing the selected boxes with score and class as tuples `[class_id, box_score, xmin, ymin, xmax, ymax]`.
+
+* **2**: `selected_indices` - tensor of type *T_IND* and shape `[number of selected boxes, 1]` the selected indices in the flattened `boxes`, which are absolute values cross batches. Therefore possible valid values are in the range `[0, num_batches * num_boxes - 1]`.
+
+* **3**: `selected_num` - 1D tensor of type *T_IND* and shape `[num_batches]` representing the number of selected boxes for each batch element.
+
+When there is no box selected, `selected_num` is filled with `0`. `selected_outputs` is an empty tensor of shape `[0, 6]`, and `selected_indices` is an empty tensor of shape `[0, 1]`.
+
+**Types**
+
+* *T*: floating point type.
+
+* *T_MAX_BOXES*: integer type.
+
+* *T_THRESHOLDS*: floating point type.
+
+* *T_IND*: `int64` or `int32`.
+
+**Example**
+
+```xml
+
+
+
+
+ 3
+ 100
+ 4
+
+
+ 3
+ 5
+ 100
+
+
+
+
+```