The recognition of objects using target acquisition systems is modeled by a sensor's minimum resolvable temperature (MRT), the Johnson criteria, atmospherics, and object specifics. Collectively, these three characteristics provide an acquisition model for estimating the probability of object recognition (and detection, identification) as a function of sensor-to-object range. This technique is called the probabilities of discrimination. When quantifying the performance of intelligence- surveillance-reconnaissance (ISR) systems, object recognition is assessed using the National Imagery Interpretability Scale (NIIRS). Each NIIRS level corresponds to a different capacity for object recognition and is defined by a set of recognition criteria. The general image quality equation (GIQE) is the ISR sensor model that determines the expected NIIRS level of a sensor for a given set of sensor parameters. It is important that electro-optical sensor engineers understand both of these recognition models. The segregation between the target acquisition and ISR sensor communities is becoming less sharp as ISR sensors are beginning to be used for target acquisition purposes and visa versa. Network and wireless communication advances are providing the means for dual exploitation of these systems. Descriptions of these two recognition models, probabilities of discrimination, and the GIQE are provided. The two models are applied to example systems. Finally, the two models are compared and contrasted.