diff --git a/docs/performance/tune-performance/profiling-tools.md b/docs/performance/tune-performance/profiling-tools.md
index 095a4ee5bc..23f6138d03 100644
--- a/docs/performance/tune-performance/profiling-tools.md
+++ b/docs/performance/tune-performance/profiling-tools.md
@@ -35,11 +35,14 @@ sess_options.enable_profiling = True
 
 If you are using the onnxruntime_perf_test.exe tool, you can add `-p [profile_file]` to enable performance profiling.
 
-In both cases, you will get a JSON file which contains the detailed performance data (threading, latency of each operator, etc). This file is a standard performance tracing file, and to view it in a user-friendly way, you can open it by using chrome://tracing:
+In both cases, you will get a JSON file which contains the detailed performance data (threading, latency of each operator, etc). This file is a standard performance tracing file, and to view it in a user-friendly way, you can open it by using multiple tools.
 
-* Open Chrome browser
-* Type chrome://tracing in the address bar
-* Load the generated JSON file
+* (Windows) Use the WPA GUI to open the trace using the Perfetto OSS plugin - [Microsoft-Performance-Tools-Linux-Android](https://github.com/microsoft/Microsoft-Performance-Tools-Linux-Android)
+* [Perfetto UI](https://www.ui.perfetto.dev/) - Successor to Chrome Tracing UI
+* chrome://tracing: 
+  * Open a Chromium based browser such as Edge or Chrome
+  * Type chrome://tracing in the address bar
+  * Load the generated JSON file
 
 To profile CUDA kernels, please add the cupti library to your PATH and use the onnxruntime binary built from source with `--enable_cuda_profiling`.
 To profile ROCm kernels, please add the roctracer library to your PATH and use the onnxruntime binary built from source with `--enable_rocm_profiling`.