The idea behind the project was to use a machine-learning algorithm to teach a computer to operate a car on the track on its own. The graphical interface is solved with the help of pyglet, a powerful python library for graphics. Car movements and collision calculations were solved using vector geometry and 2D trigonometry. I have implemented a neural network as an interface for a computer through which it can operate the car independently. The input values for the neural network were five sensors on the car that reported to the car its distance from the track edges in five different directions. In addition to the five inputs, the neural network also consists of two hidden and one output layer. All together made up of exactly 100 neurons. A genetic algorithm was used to help the neural network learn how to operate the car. GA is an evolutionary algorithm of a stochastic nature, which is great for solving more difficult optimization problems. As a result of the project, an application was obtained to demonstrate the learning of the neural network by applying the genetic algorithm to the example of driving a car along a given track. Namely, the algorithm successfully drove the easier paths, but when the path becomes complicated, the algorithm seems to get stuck at one of the local minimums and fails to complete the path.