Real-time algorithms which provide the earliest possible indication of off-nominal Space Shuttle Main Engine (SSME) conditions could improve Shuttle safety and reliability by providing more time for corrective action. Multi-parameter fault detection techniques have been targeted because they do not rely on a single parameter for fault information and thereby improve confidence in the detection. Furthermore, no assumptions regarding failure modes are required, permitting the detection of previously unencountered or unanticipated failures. The Clustering Algorithm, a multi-parameter fault detection approach that was originally trained and validated on SSME ground test firing data, was slightly modified and applied to SSME historical flight data; the application is documented in this report. Preliminary studies were conducted to assess the impact of different engines, different missions and different thrust profiles on the performance of the Clustering Algorithm. The algorithm successfully predicted sixteen performance parameters during mainstage operation of the engine when applied to nominal data sets and provided indications of off-nominal behavior when applied to data from an engine which had experienced an offset in one of the control parameters. The information from the Clustering Algorithm is intended to enhance the diagnostic information available to the NASA Johnson Space Center control room engineers during flight.