In this era of Artificial Intelligence, humans are becoming more dependent on technology. With the enhanced technology, multinational companies like Google, Tesla, Uber, Ford, Audi, Toyota, Mercedes-Benz, and many more are working on automating vehicles. They are trying to make more accurate autonomous or driverless vehicles. Information on self-driving cars is everywhere, where the vehicle itself behaves like a driver and does not need any human guidance to run on the road. This compels us to think about the safety aspects—a chance of significant accidents from machines. Many researchers are running on various algorithms to ensure 100% road safety and accuracy. One such algorithm is Traffic Sign Recognition that is proposed via this project. When we are on the road, we see various traffic signs like traffic signals, turn left or right, speed limits, no passing of heavy vehicles, no entry, children crossing, etc., that we need to follow for a safe drive. Likewise, autonomous vehicles also have to interpret these signs and make decisions to achieve accuracy. The methodology of recognizing which class a traffic sign belongs to is called Traffic signs classification. In this project, a model has been built for the classification of traffic signs available as images into many categories using a CNN and Keras library.