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We present a novel classification method for recognizing traffic sign symbols undergoing image degradations. In order to cope with the degradations, it is desirable to use combined blur-affine invariants (CBAIs) of traffic sign symbols as the feature vectors. Combined invariants allow to recognize objects in the degraded scene without any restoration. In this research, multi-class support vector machines (M-SVMs) is applied to traffic sign classification and compared with backpropagation (BP) algorithm, which has been commonly used in neural network. Experimental results indicate that M-SVMs algorithm is superior to BP algorithm both on the classification accuracy and generalization performance of the classifier.