As research continues in the development of self-driving cars, one of the key challenges is computer vision, allowing these cars to develop an understanding of their environment from digital images. In particular, this involves the ability to recognize and distinguish road signs – stop signs, speed limit signs, yield signs, and more. In this project, we use TensorFlow to build a neural network to classify road signs based on an image of those signs. For this project, we use the German Traffic Sign Recognition Benchmark (GTSRB) dataset, which contains thousands of images of 43 different kinds of road signs. This project was done as a part of the CS50AI course offered by Edx and Harvard University.