The Optical Plume Anomaly Detection (OPAD) system is under development to predict engine anomalies and engine parameters of the Space Shuttle's Main Engine (SSME). The anomaly detection is based on abnormal metal concentrations in the optical spectrum of the rocket plume. Such abnormalities could be indicative of engine corrosion or other malfunctions. Here, we focus on the second task of the OPAD system, namely the prediction of engine parameters such as rated power level (RPL) and mixture ratio (MR). Because of the high dimensionality of the spectrum, we developed a linear algorithm to resolve the optical spectrum of the exhaust plume into a number of separate components, each with a different physical interpretation. These components are used to predict the metal concentrations and engine parameters for online support of ground-level testing of the SSME. Currently, these
predictions are labor intensive and cannot be done online. We predict RPL using neural networks and give preliminary results.