Multispectral imaging has motivated new applications related to quality monitoring for industrial applications due to its capability of analysis based on spectral signatures. In practice, however, a multispectral system used for such purposes is limited because of the large amount of data to be analyzed, being necessary to develop fast methods for the unsupervised classification task. This manuscript introduces a fast and efficient algorithm that is used in combination with a multispectral system for the unsupervised classification of food based on quality. In particular, given two types of fruits previously characterized, we first register a multispectral image from them and perform a dimensionality reduction by taking into account the most representative spectral bands that involve their reflection spectra. From the reduced set, the min-W and max-M lattice associative memories are computed and a subset of their columns are used as centroids of specific clusters. Then, the Euclidean distance computed between each centroid and all spectral vectors in the image allows to subdivide the image in clusters. The achieved results state that the technique is fast, reliable, and non-invasive for food classification.