NormalBoost is a new boosting algorithm which is capable of classifying a multi-dimensional binary class dataset. It adaptively combines several weak classifiers to form a strong classifier. Unlike many boosting algorithms which have high computation and memory complexities, NormalBoost is capable of classification with low complexity. The purpose of this paper is to present NormalBoost as a framework which establishes a platform to solve classification problems. The approach was tested with a dataset which was extracted automatically from real-world traffic sign images. The dataset contains both images of traffic sign borders and speed limit pictograms. This framework involves the computation of Haar-like features of these images and then employs the NormalBoost classifier to classify these traffic signs. The total number of images which were classified was 6500 binary images. A -fold validation was invoked to check the validity of the classification which resulted in a classification rate of 98.4% and 98.9% being achieved for these two databases. This framework is distinguished by its invariance to in-plane rotation of the images under consideration. Experiments show that the classification rate remains almost constant when traffic sign images with different angles of rotations were tested.