We examine the use of mean squared error matching metrics in support of model-based automatic target recognition under the Moving and Stationary Target Acquisition and Recognition (MSTAR) program. The utility of this type of matching metric is first examined in terms of target discriminability on a 5-class problem, using live signature data collected under the MSTAR program and candidate target signature features predicted from the MSTAR signature feature prediction (MSTAR Predict) module. Analysis is extended to include the exploitation of advanced model-based candidate target signature feature prediction capabilities of MSTAR Predict, made possible by the use of probability distribution functions to characterize target return phenomenology. These capabilities include the elimination of on-pose scintillation effects from predicted target signature features and the inclusion of target pose uncertainty and intra-class target variability into predicted target signature features. Results demonstrating the performance advantages supported by these capabilities are presented.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.