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This paper explores using linear regression and Artificial Neural Networks to model the performance of an ATh algorithm based on a given set of data. Here, a probability of detection response surface as a function of relevant parameters (depression angle of a tank, age of breast cancer patient, etc.) is simulated. It is then shown that this surface can be approximated using either linear regression or an artificial neural network with good results. These regression surfaces can provide valuable information to the ATh developer/customer in terms of trying to predict ATR performance in untested areas. The application of this ATR performance modeling methodology becomes clear when we consider applying it to a common problem, such as air-to-ground target detection, where the changing parameters of the target can give a good set of data points from which to build the response curve.