Image Formation in the Environment of a Mobile Robot''''s Choice and Their Classification Using Neural Networks and Logical-Linguistic Classification Algorithms. The article investigates the task of improving the accuracy and speed of image classification for mobile robot and UAV control systems under conditions of data uncertainty. An integrated approach combining fuzzy logic methods (logical-linguistic classification (LLC)) and neural network technologies is proposed. A mathematical model for image representation using attribute membership functions has been developed, allowing it to work with noisy and incomplete data. An algorithm for generating test data based on etalon images with an adjustable noise level (0 − 100%) was created. A comparative testing of the neural network approach and the LLC algorithm was conducted on sample sizes ranging from 680 to 68 000 images. It was experimentally established that the neural network demonstrates high efficiency with large data volumes and high noise levels (> 80%), while the LLC algorithm is more effective with small samples and moderate noise levels (50 − 60%). The minimum training sample size for stable operation of the neural network was determined to be 6 800 images. The practical significance of the work lies in the development of an adaptive classification system capable of operating in real-world conditions of robotic complexes with variable levels of informational uncertainty.