This report will be of interest to railcar maintenance professionals concerned with improving railcar maintenance fault-diagnostic capabilities through the use of artificial intelligence (AI) technologies. The report documents the results of a demonstration of an AI-based program that acts as a diagnostic assistant for transit railcar propulsion systems. The diagnostic program uses a hybrid AI approach with both model-based reasoning and expert system rules. The AI tool was tested at the Washington Metropolitan Area Transit Authority (WMATA) on direct current chopper propulsion systems of the 3000 series railcars. The system was determined to be easy to use and effective in diagnosing propulsion system faults.