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Purpose. Investigation of the possibilities of increasing the accuracy and variability of recognition of different groups of objects with similar redundant information by a neural network, after preprocessing and grouping of training images for further use on microcontrollers. The effect of removing redundant information in the training data on the practical values of recognition accuracy of different categories of objects based on the MobileNet V2 architecture is verified. Methodology. Creating different groups of training images using the Edge Impulse software platform. Image processing by the method of removing unnecessary objects in the graphic editor Adobe Photoshop. Testing the recognition accuracy of raw, processed and mixed groups of training images. Findings. Several groups of training images have been created on the basis of the Edge Impulse software platform. In part of the images, all unnecessary elements were removed using the Adobe Photoshop graphic editor. It was established that in the presence of similar redundant information on the training data, in different categories of objects, unrecognized and falsely recognized test images appear as a result of recognition. It was experimentally found that the method of removing redundant information from training images gives a clearer distribution of features, and the combination of raw and processed training data gives an average increase in recognition accuracy of more than 10% for each category, as well as a significant reduction of unrecognized and incorrectly recognized test images, while the same amount of training data. Originality. A combined method of training data processing and grouping was obtained, which increases the accuracy of object recognition without increasing the amount of training data. The effect of similar redundant information in different categories of objects on recognition accuracy was investigated. Practical value. The obtained results make it possible to increase the accuracy of recognition of different groups of objects with similar redundant information by one neural network without increasing the number of training images. [ABSTRACT FROM AUTHOR]