The signature of a target imaged by a millimeter-wave SAR is highly variable. Various viewing angles will cause different scattering centers to be illuminated, the returns from which can vary greatly with minor changes in viewing angle, and the coherence of the radiation induces speckle noise. Using fully polarimetric turntable (inverse SAR) data, we have undertaken some basic investigations of the persistence of scatterers as a function of azimuth for a number of depression angles from 15 degrees to 32 degrees. Although many scatterers persist for only a few degrees of azimuth, enough persist for 10 to 20 degrees to make model-based recognition feasible. Based on these results, we have developed an experimental system for target recognition. The system uses the functional template approach for detection, pose estimation, and initial hypothesis ranking. The best-matching template defines an area where so-called bright-points are extracted, resulting in a binary feature map that shows the location of strong scatterers. Back-end recognition consists of matching these feature maps to target appearance models that capture the location of scatterers that produce strong returns and are sufficiently persistent with changes in viewing angle. The performance of the hypothesis generation via functional templates is briefly reviewed, both for ISAR data and for SAR data. Recognition results obtained with the new back-end recognition system are also presented for the case of ISAR data.