This study concerns the development of a new system to detect meat and bone meal (MBM) in compound feeds, which will be used to enforce legislation concerning feedstuffs enacted after the European mad cow crisis. Focal plane array near-infrared (NIR) imaging spectroscopy, which collects thousands of spatially resolved spectra in a massively parallel fashion, has been suggested as a more efficient alternative to the current methods, which are tedious and require significant expert human analysis. Chemometric classification strategies have been applied to automate the method and reduce the need for constant expert analysis of the data. In this work the performance of a new method for multivariate classification, support vector machines (SVM), was compared with that of two classical chemometric methods, partial least squares (PLS) and artificial neural networks (ANN), in classifying feed particles as either MBM or vegetal using the spectra from NIR images. While all three methods were able to effectively model the data, SVM was found to perform substantially better than PLS and ANN, exhibiting a much lower rate of false positive detection. Copyright © 2004 John Wiley & Sons, Ltd.