Abstract text. This paper considers several options for the architecture of convolutional neural networks for the recognition of isolated handwritten Ukrainian characters and numbers, which were trained using a synthetic dataset built on the basis of a set of handwritten and cursive fonts. Comparison of the results of recognition of several variants of images containing handwritten letters and numbers using neural networks with different architectures showed that an increase in the number of convolutional layers leads to a decrease in the frequency of erroneous character recognition. The size of the training dataset significantly affects the reliability of character recognition. The data sets used in the work contained from 192 to 2304 samples per class. The upper limit of the number of samples per class is close to the limit that provides acceptable recognition accuracy. Reducing the sample size by reducing the number of samples per class leads to a significant decrease in recognition accuracy (from 90% recognition accuracy of elements of real inscriptions to 40-60% with a 4-fold decrease in sample size).