Multispectral imaging has given place to important applications related to classification and identification of
objects from a scene. Because of multispectral instruments can be used to estimate the reflectance of materials
in the scene, these techniques constitute fundamental tools for materials analysis and quality control. During
the last years, a variety of algorithms has been developed to work with multispectral data, whose main purpose
has been to perform the correct classification of the objects in the scene. The present study introduces a
brief review of some classical as well as a novel technique that have been used for such purposes. The use of
principal component analysis and K-means clustering techniques as important classification algorithms is here
discussed. Moreover, a recent method based on the min-W and max-M lattice auto-associative memories, that
was proposed for endmember determination in hyperspectral imagery, is introduced as a classification method.
Besides a discussion of their mathematical foundation, we emphasize their main characteristics and the results
achieved for two exemplar images conformed by objects similar in appearance, but spectrally different. The
classification results state that the first components computed from principal component analysis can be used to
highlight areas with different spectral characteristics. In addition, the use of lattice auto-associative memories
provides good results for materials classification even in the cases where some spectral similarities appears in
their spectral responses.