Authors - Jitesh Kumar S, Naveen Santhanam, Jaisakthi S M Abstract - Traffic sign recognition system plays a crucial role in vehicles that assures the safety of humankind. The system provides feedbacks on road information to the drivers on time which helps him in driving the car without accidents. Deep Neural Networks (DNN) are gaining more attention in the field of image analysis since it produces high accuracy rate. Recently many researchers are focusing on recognition of traffic sign using DNN to get better results. So, in this paper we have applied transfer learning technique to classify traffic signs. We choose to work with 2 architectures to classify road signs namely _ne tuned Convolutional Neural Network(CNN) and Resnet50. The two architectures were trained using the GTSRB - German Traffic Sign Recognition Benchmark downloaded from kaggle. With these models we have obtained a good result of 98% and 75% respectively.