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A generic, transform-domain image classification method is presented and applied to the fingerprint verification problem. At first, the image is decomposed by a bank of Gabor filters and, at every pixel, its spectral information is extracted in vectorial form. In order to reduce redundancy, a neural-based vector quantizer is used to select representative samples that encode the multivariable fingerprint spectral distribution. Similarity between image distributions, utilized as a distance measure by the classification task, is then assessed in pairwise form by means of a non-parametric statistical test between the corresponding code-vectors. The presented multi-scale vectorial representation allows the inclusion of higher order dependencies among image pixels that describe in a unique way individual features of fingerprint images.