Advanced driver assistance systems are an indispensable part of modern vehicles. The traffic sign recognition system enables the development of autonomous vehicles and the reduction of the number of traffic accidents caused by non-compliance with traffic rules. Traffic signs differ according to the color and shape of the sign and the symbols on the sign itself. Methods in the field of artificial intelligence, i.e., computer vision, are used to recognize traffic signs. Preprocessing and augmentation methods are used to prepare data for learning the model. The recognition system consists of two models, one for detecting and one for classifying traffic signs. The YOLO algorithm was used to detect traffic signs and predict their location. Convolutional neural networks were used to classify traffic signs into 43 categories from cut out bounding boxes. Traffic sign recognition is performed on real videos, and the recognition system was evaluated at different vehicle speeds, different light intensities, on different traffic signs in different preservation conditions and from different observation positions. The evaluation showed high accuracy in the operation of the system, but also pointed out problems working in outdoor environments and opportunities for improvement.