This paper shows the possibility of separating and classifying remotely-sensed multispectral data from rocks and minerals onto seven geological rock-type groups. These groups are extracted from the general categories of metamorphic, igneous and sedimentary rocks. This study is performed under ideal conditions for which the data is generated according to laboratory hyperspectral data for the members, which are, in turn, passed trough the Multispectral Thermal Imager (MTI) filters yielding 15 bands. The main challenge in separability is the small size of the training data sets, which initially did not permit the reliable estimation of the second-order statistics for every class. To enable Bayesian classification, the original training data is linearly perturbed with the addition of minerals, vegetation, soil, water and other valid impurities. As a result, the size of the training data is significantly increased and estimates of the covariance matrices are obtained. An eigenvalue analysis is used to generate a set of reduced (five) multispectral vectors, viz., feature vectors, providing principal information about the data. In addition, a nonlinear band-selection method is also employed, based on spectral indices, comprising a small subset of all possible ratios between bands. By applying three optimization strategies, optimal combinations of two and three ratios are found that provide reliable separability and classification between all seven groups. To set a benchmark to which the MTI capability in rock classification can be compared, an optimization strategy is performed for the selection of optimal multispectral filters, other than the MTI filters, and an improvement in classification is predicted when these filters are used.