A prototype automated forward looking infrared (FLIR) minimum resolvable temperature difference (MRTD) evaluation software system was developed and tested. After data capture and preliminary image processing of FLIR 4-bar target imagery, the boundary contour system (BCS) model of the human early vision system was coupled with a custom feature extractor to produce a set of features characteristic of those employed by humans during detection tasks. These feature sets, along with known target visibility, were used to train a fuzzy adaptive resonance theory MAP (ARTMAP) decision algorithm to emulate human observer performance in determining MRTD as a function of target to background contrast and target spatial frequency. During prototype system evaluation, the system was trained on 180 pairs of input imagery and human observer response data (resolvable/not-resolvable), and then tested against another 60 input images without the human judgments. The system predictions of human response to the test images were than compared to actual human response decisions for the images. Prototype success rates in the range of 96% to 100% were achieved in correctly predicting human response MRTD decisions in a low fidelity situation.