A key concern in animal feed factories is guaranteeing the correct labeling of compound feeds. Therefore, due to incorrect labeling, there is an urgent need for new control methods on the claims that can be made. In this study, this question has been tackled with different multivariate classification algorithms based on the near-infrared spectral fingerprint obtained from a given compound feed analyzed in its original physical market presentation form (i.e., cubes, coarse meals, pellets). The objective of this paper is the evaluation of different methods for establishing a separation among 24 feed types. Two linear methods, soft independent modeling of class analogy (SIMCA) and partial least squares (PLS) with two approaches to classification (PLSD and PLS-LDA); and one nonlinear method, support vector machines (SVM), were studied. The database used had the following structure: a first division was made between granules and meals; within these two groups, there was a second division according to three animal species to which the feed was marketed (bovine, ovine, and porcine); within each species there was a third division according to the age or physiological status of the animal (i.e., lactating dairy cattle, starters, etc.). Given the database structure, all the methods were evaluated following two strategies: (1) development of a model composed of the nine classification models corresponding to the structure of the data; and (2) development of a unique model that discriminates among the 24 classes of different feeds. With both strategies the lowest percentage of misclassified samples was achieved with the SVM method (3.96% with strategy 1 and 2.31% with strategy 2). Among the linear methods evaluated, SIMCA yielded the best results, with a percentage of 8.47% misclassified samples with strategy 1 and 4.05% misclassified samples with strategy 2. The results in this study show the ability of near-infrared spectroscopy to make acceptable classifications of feed types based only on spectral information, with differences in performance depending on the multivariate algorithm used.