Automatic target recognition (ATR) functionality clearly relies in some way on its knowledge of what the objects and classes of interest look like. The ‘‘comparison’’ of this knowledge to the current information on a new object determines the results of the recognition process. If the new object information or the original training knowledge base is distorted by compression or other means, there is a high potential for degraded ATR performance. The effect is clear for ATR because a consistent signature, assuming of course the signature is a ‘‘good’’ one, helps the ATR, where an inconsistent signature has detrimental performance effects. Lossy compression methods vary significantly as to the type and amount of distortion they introduce. We show the distortion comparison of 15 contractor/industry methods on 8 and 12 bit data sets quantitatively over varying ranges of compression and make obvious the compression methods that distort the target spatial signature and those that tend to minimize this distortion.