Near infrared reflectance (NIR) spectroscopy was used in combination with chemometrics to discriminate between fishmeal, meat meal and soya meal samples. Samples were obtained from commercial feed miles and scanned in the NIR region (1100 - 2500 nm) in a monochromatic instrument in reflectance mode. Principal component analysis (PCA) and linear discriminant analysis were used to classify samples based on their NIR spectra. Full cross-validation was used in the development of classification models. Partial least squares-discriminant analysis (PLS-DA) correctly classified 85.7% of the fishmeal samples and 100% of the meat meal and soya meal samples. These results demonstrate the usefulness of NIR spectra combined with chemometrics as an objective and rapid method to classify fishmeal, meat meal and soya meal samples. NIR spectroscopic methods can be easily implemented in food miles and may be most useful for initial screening at early stages in the food production chain, enabling more costly methods to be used selectively for suspected specimens.