Abstract <p>Formation of features of microstructure images for their subsequent classification often encounters difficulties due to the high variability of images caused by changes in lighting, angle, and scale. Classifying images after feature discovery is also complicated by intraclass variability, where objects of the same class can differ greatly from each other, and interclass similarity, where objects of different classes are similar to each other, which also complicates the task. This paper proposes the formation of image features based on their topological decomposition. The use of the proposed approach makes it possible to avoid the problems described above since the generated features do not depend on the variability associated with changes in the brightness and geometric properties of images. The study was conducted on two datasets: images of metals containing 6 classes and images of polyvinyl alcohol cryogels containing 20 classes. The conducted testing showed that the classification accuracy when using the proposed approach is 94.11% on metals and 80.61% on polyvinyl alcohol cryogels. When using neural networks, the accuracy was 92.16 and 55.45%, respectively. Experiments have shown a preference for using the proposed approach when there is interclass similarity in the dataset.</p>