We have investigated how observers learn to classify compound Gabor signals as a function of their differentiating frequency components. Performance appears to be consistent with decision processes based upon the least squares minimum distance classifier (LSMDC) operating over a cartesian feature space consisting of the real (even) and imaginary (odd) components of the signals. The LSMDC model assumes observers form prototype signals, or adaptive filters, for each signal class in the learning phase, and classify as a function of their degree of match to each prototype. The underlying matching process can be modelled in terms of cross-correlation between prototype images and the input sample.