Fundamental to the model-based paradigm of an Automatic Target Recognition (ATR) system is an accurate representation (a model) of the physical objects to be recognized. Detailed CAD models of targets of interest can be created using photographs, blueprints, and other intelligence sources. When created this way, the target CAD models are necessarily specific to a particular realization of the vehicle (namely, the serial number of the vehicle from which the CAD model was validated). Under realistic battlefield conditions, variations across targets of the same type (i.e. T72) may be quite drastic and may manifest themselves as significant differences in the sensor signatures. Given this variability between targets of the same type, the example CAD model, or 'exemplar' model, may not provide an adequate representation of the vehicle across the entire class. This paper discusses the development of class models for use in a model-based ATR for synthetic aperture radar (SAR). It documents the propagation of variability information into feature uncertainty, and comments on the performance of class models in the Moving and Stationary Target Acquisition and Recognition (MSTAR) model- based ATR system.