Boundary segmentation has long been a problem for automatic target recognizers. Its performance is crucial because it serves as the front end to the entire system. The authors examine and compare the characteristics and capabilities of four segmentors: the Boundary Contour System, the Meyer Line Finder, the Canny, and the Sobel Edge Detector. The first three models are 'smart' systems, that is, they have some 'higher level' processing capability, while the Sobel is a simple operator. In addition, the Boundary Contour System is neural based while the remaining three are conventional. The performance of each segmentor is evaluated with respect to the following image metrics: signal-to-noise, contrast, resolution, and the following boundary characteristics: spatial frequency, edge orientation. Both computer and terrain board modeled infrared imagery is used. Performance is quantified through both segmentation accuracy measures and visual fidelity.