Synthetic aperture radar (SAR) is an important tool for wide-area surveillance since it can provide all-weather, day/night coverage. The surveillance of large areas implies a large number of SAR images to be analyzed per unit time. Target detection and recognition algorithms can potentially ease this workload by focusing the analysts' attention on important parts of the collected imagery. Automatic target detection and recognition are challenging because, in SAR imagery, the target signatures can vary significantly with viewing angle. The clutter backgrounds against which targets may be placed can also vary drastically, from open fields to urban streets. Furthermore, because SAR data is collected and processed coherently, target signatures and clutter backgrounds are corrupted with a speckle noise component. Model-based target recognition represents a spectrum of approaches to the problem of detecting and identifying targets of interest in large volumes of data. The basic paradigm consists of detecting and extracting features that are used to make initial hypotheses about target identities and states. Based on those working hypotheses, target signatures are predicted and compared to image-derived data. If the comparison is good, the target is `recognized.' If not, the working hypothesis is refined and used to improve the predicted signature. If, at some point, it is concluded that no predicted signature adequately represents the data, then the object in question is declared `unknown.' In this paper we highlight several of the important issues in developing model-based target recognition algorithms for SAR imagery. We discuss signature representation, hypothesis generation, feature prediction, and evidential reasoning. Our goal is to highlight these issues and any controversies surrounding them, rather than discuss a particular approach to developing a model-based recognition system.