Applying model-based vision techniques to SAR data is particularly challenging because of the inherent difficulty in generating accurate predictions of an electromagnetic signature and the variation of observed signatures to small changes in sensing conditions, imaging geometry, and object characteristics. In order to cope with these difficulties we are developing a robust feature matching model to be part of the moving and stationary target acquisition and recognition model-based automatic target recognition system. The goals of this matching module are: (1) generate correspondences between predicted features and features extracted from a SAR image, (2) evaluate the match based on the degree of uncertainty of the features and their degree of match, (3) refine the target position/orientation/articulation based on the feature correspondences, and (4) analyze residual mix- matches for cueing scene interpretations of unexplained image features. We are developing a probabilistic optimization matching approach based on a (1) Bayesian evaluation metric and (2) they dynamic solution of the best correspondences during the search of pose space. The system is designed to support a wide range of features (points, regions, and other composite features) in a wide range of situations, such as obscuration, attenuation, layover, and variable target articulations and configurations. Initial test results in these types of situations are presented.