A fast method to detect and recognize scaled and skewed road signs is proposed in this paper. The input color image is first quantized in HSV color model. Border tracing those regions with the same colors as road signs is adopted to find the regions of interest (ROI). Verification is then performed to find those ROIs satisfying specific constraints as road sign candidates. The candidate regions are extracted and normalization is automatically calculated to handle scaled and skewed road signs. Finally, matching based on distance maps is adopted to measure the similarity between the scene and model road signs to accomplish recognition. Experimental results show that the proposed method is effective and efficient, even for scaled and skewed road signs in complicated scenes. On the average, it takes 4-50 and 11 ms for detection and recognition, respectively. Thus, the proposed method is adapted to be implemented in real time.