diff --git a/tools/mo/openvino/tools/mo/front/tf/placeholder_ext.py b/tools/mo/openvino/tools/mo/front/tf/placeholder_ext.py
index bb0f24f6fd8..1628bb6067f 100644
--- a/tools/mo/openvino/tools/mo/front/tf/placeholder_ext.py
+++ b/tools/mo/openvino/tools/mo/front/tf/placeholder_ext.py
@@ -1,10 +1,13 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-from openvino.tools.mo.ops.parameter import Parameter
+import logging as log
+
+from openvino.tools.mo.front.common.partial_infer.utils import shape_array
 from openvino.tools.mo.front.extractor import FrontExtractorOp
 from openvino.tools.mo.front.tf.extractors.utils import tf_dtype_extractor, tf_tensor_shape
 from openvino.tools.mo.ops.op import PermuteAttrs
+from openvino.tools.mo.ops.parameter import Parameter
 
 
 class PlaceholderFrontExtractor(FrontExtractorOp):
@@ -13,9 +16,29 @@ class PlaceholderFrontExtractor(FrontExtractorOp):
 
     @classmethod
     def extract(cls, node):
+        shape = shape_array([])
+        # Extract output shape from `shape` attribute
+        extracted_shape = tf_tensor_shape(node.pb.attr["shape"].shape)
+        if len(extracted_shape) != 0:
+            shape = extracted_shape
+        else:
+            # Extract output shape from `_output_shapes` attribute if it is possible
+            extracted_output_shapes = node.pb.attr["_output_shapes"].list.shape
+            if len(extracted_output_shapes) == 1:   # check if attribute not empty
+                extracted_output_shapes = tf_tensor_shape(extracted_output_shapes[0])
+
+                # Check equality of extracted shapes. We know some cases then Placeholder operation has empty `shape`
+                # attribute value and non-empty `_output_shapes` attribute value and need co handle and support it.
+                if len(extracted_output_shapes) > len(extracted_shape):
+                    log.warning('Extracted shapes for Placeholder operation {} have different lengths: `shape` {} and '
+                                '`_output_shapes` {}. Please, check if model is consistent'.format(
+                        node.pb.name, extracted_shape, extracted_output_shapes))
+                    if len(extracted_output_shapes) != 0:
+                        shape = extracted_output_shapes
+
         attrs = {
             'data_type': tf_dtype_extractor(node.pb.attr["dtype"].type),
-            'shape': tf_tensor_shape(node.pb.attr["shape"].shape),
+            'shape': shape,
             'permute_attrs': PermuteAttrs().update_attrs(attrs=[('shape', 'output:0')])
         }
         if node.pb.attr["shape"].shape.unknown_rank: