A well designed performance modeling tool can assess trade- offs in sensor parameters for design; reasonably predict performance for varied targets, geometries, backgrounds, and environments; diagnose sensor system and signal processing problems, and provide a valued remote sensing educational tool. This paper describes the Infrared Performance Model (IRPM) which predicts IRST (Infrared Search and Track) detection SNRs for ranges of user specified operational scenarios, sensor design parameters, target and background models, and signal processing options. IRPM is a GUI driven software system which provides automated experimentation to graphically show the impacts of design or scenario parameter variations. Notable features include options for calculating radiometrically accurate target signatures from smoothed facet models, MTF calculation, system noise calculation from detector parameters, analytic Butterworth clutter models as well as clutter PSDs estimated from data, and sensor imperfections including aliasing, pattern noise, and jitter. The signal processing options include 2D and 3D matched filters and frame differencing detectors. Included is a discussion of the IRPM algorithms and sample results from the verification of IRPM for the AIRMS program.