diff --git a/src/frontends/pytorch/src/op_table.cpp b/src/frontends/pytorch/src/op_table.cpp
index 18063f87c0a..1ac094d991a 100644
--- a/src/frontends/pytorch/src/op_table.cpp
+++ b/src/frontends/pytorch/src/op_table.cpp
@@ -468,6 +468,11 @@ const std::map<std::string, CreatorFunction> get_supported_ops_ts() {
         {"aten::repeat_interleave", op::translate_repeat_interleave},
         {"aten::reshape", op::translate_reshape},
         {"aten::reshape_as", op::translate_reshape_as},
+        // TO DO: enable behaviour for resolve_conj and resolve_neg complex tensors,
+        // when complex dtype will be supported
+        // for real dtypes, these operations return input tensor without changes and can be skipped
+        {"aten::resolve_conj", op::skip_node},
+        {"aten::resolve_neg", op::skip_node},
         {"aten::roll", op::translate_roll},
         {"aten::round", op::translate_round},
         {"aten::rsqrt", op::translate_rsqrt},
diff --git a/tests/layer_tests/pytorch_tests/test_resolve_conj_neg.py b/tests/layer_tests/pytorch_tests/test_resolve_conj_neg.py
new file mode 100644
index 00000000000..87097b88af3
--- /dev/null
+++ b/tests/layer_tests/pytorch_tests/test_resolve_conj_neg.py
@@ -0,0 +1,54 @@
+# Copyright (C) 2018-2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import pytest
+
+from pytorch_layer_test_class import PytorchLayerTest
+
+
+class TestResolveConjNeg(PytorchLayerTest):
+    def _prepare_input(self, dtype="float32"):
+        import numpy as np
+        return (np.random.randn(2, 4).astype(dtype),)
+
+    def _prepare_input_complex(self):
+        import numpy as np
+        return (np.array([[2+3j, 3-2j, 4-9j,10+1j], [1-3j, 3+2j, 4+9j,10-5j]]), )
+
+
+    def create_model(self, op_type):
+        import torch
+        
+        ops = {
+            "resolve_conj": torch.resolve_conj,
+            "resolve_neg": torch.resolve_neg
+        }
+
+        op = ops[op_type]
+
+        class aten_resolve(torch.nn.Module):
+            def __init__(self, op):
+                super(aten_resolve, self).__init__()
+                self.op = op
+
+            def forward(self, x):
+                return self.op(x)
+
+        ref_net = None
+
+        return aten_resolve(op), ref_net, f"aten::{op_type}"
+
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    @pytest.mark.parametrize("op_type", ["resolve_neg", "resolve_conj"])
+    @pytest.mark.parametrize("dtype", ["float32", "int32"])
+    def test_reslove(self, op_type, dtype, ie_device, precision, ir_version):
+        self._test(*self.create_model(op_type), ie_device, precision, ir_version, kwargs_to_prepare_input={"dtype": dtype})
+
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    @pytest.mark.parametrize("op_type", ["resolve_neg", "resolve_conj"])
+    @pytest.mark.xfail(reason="complex dtype is not supported yet")
+    def test_resolve_complex(self, op_type, ie_device, precision, ir_version):
+        self._prepare_input = self._prepare_input_complex
+        self._test(*self.create_model(op_type), ie_device, precision, ir_version)