In this paper, use of angle and radius information for feature space classification is proposed. The performance of the classification using either angle or the radius was evaluated on two different feature spaces for three and four-class classification problems. The results were compared with the well-known K-Nearest Neighbor (K-NN) and Naïve Bayes (NB) algorithms in terms of the ability to classify the feature space and classification time. Results show that angle and radius-based classification could generate better classification performances, especially when there are few training vectors available. Moreover, proposed methods were computationally more efficient than K-NN and NB algorithms. However, optimum combination of angle and radius-based classification is needed for developing a general classifier which will perform well in classification of different feature patterns.