Abstract: The classification of rocks is an inherent part of modern geology. The manual identification of rock samples is a time-consuming process, and—due to the subjective nature of human judgement—burdened with risk. In the course of the study discussed in the present paper, the authors investigated the possibility of automating this process. During the study, nine different rock samples were used. Their digital images were obtained from thin sections, with a polarizing microscope. These photographs were subsequently classified in an automatic manner, by means of four pattern recognition methods: the nearest neighbor algorithm, the K-nearest neighbor, the nearest mode algorithm, and the method of optimal spherical neighborhoods. The effectiveness of these methods was tested in four different color spaces: RGB, CIELab, YIQ, and HSV. The results of the study show that the automatic recognition of the discussed rock types is possible. The study also revealed that, if the CIELab color space and the nearest neighbor classification method are used, the rock samples in question are classified correctly, with the recognition levels of 99.8%. [Copyright &y& Elsevier]