This paper considers the importance of sensor models to model-based recognition applications. The impact of the explicit representation of sensor models and of the sensor attributes themselves (e.g., the particular geometric transformation, whether active or passive, specular or diffuse, reflective or emissive) are illustrated using synthetic aperture radar, infrared, and CO2 laser radar target recognition examples. The model-based recognition problem is formalized using probability theory to partition the recognition process into (1) an estimation where the situation parameters (e.g., sensor, target, background) are estimated and (2) a hypothesis test where the current hypothesis (i.e., the constrained model given the estimated parameter values) is tested based on the sensed data. It is shown that strong sensor models suggest problem structure that can be exploited to develop robust indexing and model refinement / parameter estimation algorithms. It is also shown that strong sensor models form a basis for rigorous match evaluation during the hypothesis test phase of the recognition process.