We develop and extensively test a new algorithm for discriminating man-made objects from natural clutter in synthetic-aperture radar (SAR) imagery. The novel feature of our approach is its exploitation of the characteristically distinct variations in speckle pattern for imagery of man-made objects and of natural clutter, as image resolution is varied from coarse to fine. We treat these characteristics using stochastic framework, specifically tailored for multiresolution random processes and fields. Within the framework, we build a pair of multiscale models: one for SAR imagery of natural clutter and another for imagery of man-made objects. We then use these models to define a multiresolution discriminant as the likelihood ratio for distinguishing between the two image types, given a multiresolution sequence of images of a region of interest (ROI). We incorporate this likelihood ratio into an existing, established discriminator that was developed at Lincoln Laboratory (LL) as part of a complete system for automatic target recognition (ATR). To classify a given ROI, we merge the information provided by our likelihood ratio with the measured values of a small number of size and brightness features. We have applied the resulting new discriminator to an extensive data set of 0.3-meter resolution, HH polarization imagery gathered with the LL millimeter-wave SAR. The detection results are extremely good. In particluar, the new discriminator achieves a significant improvement in receiver operating characteristics, compared to an optimized version of the standard discriminator that is traditionally used in the LL ATR system. This result conclusively demonstrates that multiresolution methods have an effective and important role to play in SAR ATR algorithms.