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In the meat industry the fat portions coming from two different subcutaneous layers, i.e., inner and outer, are destined to the manufacturing of different products, hence the availability of cheap, rapid and affordable methods for the characterization of the overall fat quality is desirable. In this work the potential usefulness of three techniques, i.e. tristimulus colorimetry, FT-NIR spectroscopy and NIR hyperspectral imaging, were tested to rapidly discriminate fat samples coming from the two different layers. To this aim, various multivariate classification methods were used, also including signal processing and feature selection techniques. The classification efficiency in prediction obtained using colorimetric data did not reach excellent results (78.1%); conversely, the NIR-based spectroscopic methods gave much more satisfactory models, since they allowed to reach a prediction efficiency higher than 95%. In general, the samples of the outer layer showed a high degree of variability with respect to the samples of the inner layer. This is probably due to a greater variability of the outer samples in terms of fatty acid composition and water amount.