Self-organizing maps, together with their varieties, are one of the most popular neural network architectures focused on unsupervised learning. They are widely used in fields such as statistics, signal processing, control theory, financial analysis, experimental physics, chemistry, medicine, to solve complex, multidimensional, nonlinear problems related to feature extraction, image processing and classification, adaptive control, etc. The book provides a detailed description of the mathematical apparatus and applications for self-organizing maps. For specialists in the theory and applications of neural network modeling, as well as students and graduate students of relevant specialties.