All automatic target recognizers (ATRs) either explicitly or implicitly use models and compute features, many use feedback, and all rely on some form of data for development and test. This paper compares three popular paradigms used for ATR--the prescreen, segment, classify (PSC) paradigm (commonly used when applying statistical pattern recognition techniques to image sensors), the matched filter (MF) paradigm, and the model-based vision (MBV) paradigm. This comparison is performed initially by considering how each contends with the ATR model space, which is introduced to aid the paradigm comparison. These paradigms are then compared in detail by examining how each uses models, features, feedback, and data to perform target recognition. Based on these discussions, three ATR sensors are examined in terms of the ATR model space to analyze how each sensor's imaging properties either help or hinder the solution of the ATR problem. The ATR model space concept is then used to motivate model-based vision solutions to the multi-sensor fusion problem and to suggest novel sensor combinations that could be used synergistically to attack the ATR problem.