Singer identification is the process of identifying or recognizing the singers based on the uniqueness in their singing voice. It is a challenging task in music information retrieval because of the combined instrumental music with the singing voice. The work presented in this paper recognizes a singer using Mel Frequency Cepstral Coefficient (MFCC) features and Chroma-Reduced Pitch (CRP) features with Support Vector Machines (SVM). The proposed technique for singer identification has two phases: feature extraction and identification. During the feature extraction phase, MFCC and CRP features are extracted from the songs in a database of popular music. In the second phase, the extracted features are trained with the SVM classifier. To evaluate our work, a dataset of 50 music clips was tested against the trained models of various singers. An equal error rate of 8% and 56% is achieved with SVM using MFCC and CRP features, respectively. By combining MFCC and CRP features at score level, an EER of 6.0% is obtained which indicates a significant increase in identification rate.