This paper presents a new computer vision design flow for real time detection and recognition of traffic signs. Autonomous traffic sign detection can play a crucial role in many applications related to transportation safety and geographical information systems. The challenges that need to be addressed include the necessity for robust and accurate detection as well as the high computational requirements of the algorithms. Therefore, we develop a three stage algorithm that is based on i) detection of traffic sign locations using HSV color space, ii) detection of traffic signs using discriminative features and iii) recognition of traffic signs using interest point descriptors. The results show a robust detection and recognition performance for multiple signs and the algorithm can be executed in real-time. road signs. Although image processing plays a central role in the road signs recognition, especially in colour analysis, but the paper points to many problems regarding the stability of the received information of colours, variations of these colours with respect to the daylight conditions, and absence of a colour model that can led to a good solution. This means that there is a lot of work to be done in the field, and a lot of improvement can be achieved. Neural networks were widely used in the detection and the recognition of the road signs. The majority of the authors used neural networks as a recognizer, and as classifier. Some other techniques such as template matching or classical classifiers were also used. New techniques should be involved to increase the robustness, and to get faster systems for real-time applications.