Recognizing a target in SAR images is an important, yet challenging application of model-based vision. This paper describes a model-based SAR recognition system based on invariant histograms and deformable template matching techniques. An invariant histogram is a histogram of invariant values defined by geometric features such as points and lines in SAR images. Although a few invariances are sufficient to recognize a target, we histogram all invariant values given by all possible target feature pairs. This redundant representation enables robust recognition under severe occlusions typical of SAR recognition scenarios. Multi-step deformable template matching examines the existence of an object by superimposing templates over potential energy field generated from images or primitive features. It determines the template configuration which has the minimum deformation and the best alignment of the template with features. The deformability of the template absorbs the instability of SAR features. We have implemented the system and evaluated the system performance using hybrid SAR images, generated from synthetic model signatures and real SAR background signatures.