In this paper, we develop a novel approach to target classification in synthetic aperture radar (SAR) imagery. In contrast to the conventional approach, in which grayscale test images are compared to templates using a mean-square error (MSE) criterion, we coarsely quantize the grayscale pixel values and then conduct maximum-likelihood (ML) classification using simple, robust statistical models. The advantage of this approach is that coarse quantization can preserve a great deal of discriminating information while simultaneously reducing the complexity of the statistical variation target SAR signatures to something that can be characterized accurately. We consider two distinct quantization schemes, each having its own merits. The first preserves the contrast among the target, shadow and background regions while sacrificing the target region's internal structural detail; the second preserves the target's shape and internal structural detail while sacrificing the contrast between the shadow and background regions. We postulate statistical models for the conditional likelihood of quantized imagery (one model per quantization scheme), identify model parameters from data, and then build and test ML target classifiers. For a number of challenging ATR problems examined in DARPA's Moving and Stationary Target Acquisition and Recognition (MSTAR) program, these ML classifiers are found to lead to significantly better classification performance than that obtained with the MSE metric, and as good or better than that obtained with virtually all competing MSTAR-developed approaches.