Microwave imaging techniques, in particular synthetic aperture radar (SAR), are able to obtain useful images even in adverse weather or darkness, which makes them suitable for target position or feature estimation. However, typical SAR imagery is not informative for the operator, because it is synthesized using complex radio signals with greater than 1.0 m wavelength. To deal with the target identification issue for imaging radar, various automatic target recognition (ATR) techniques have been developed. One of the most promising ATR approaches is based on neural network classification. However, in the case of SAR images heavily contaminated by random or speckle noises, the classification accuracy is severely degraded because it only compares the outputs of neurons in the final layer. To overcome this problem, this paper proposes a self organized map (SOM) based ATR method, where the binary SAR image is classified using the unified distance matrix (U-matrix) metric given by the SOM. Our numerical analyses and experiments on 5 types of civilian airplanes, demonstrate that the proposed method remarkably enhances the classification accuracy, particular in lower S/N situations, and holds a significant robustness to the angular variations of the observation.