In this paper, an efficient method for detecting and recognizing road signs from real world scenes is presented. The main outline of this method is composed of three parts: detecting road signs, i.e. road sign segmentation, shape recognition of segmented areas, and identifying the meaning of the detected signs in a hierarchical method. We employed a texture, an RG, and a BY color opponent image for road sign segmentation, a few grid lines and a bounding box for recognizing segmented sign shapes, and compressed eigenspace representation for identifying detected signs. This method has proven to be very efficient, robust, and easy to implement. Furthermore, this method can overcome substantial amount of rotation of the detected sign and opens a great possibility for more improvement and real-time usage.