This paper presents an automatic speaker verification system based on the hybrid GMM-SVM model working in real environment. An important step in speaker verification is extracting features that best characterized the speaker. Mel-Frequency Cepstral Coefficients (MFCC) and their firt and second derivatives are commonly used as acoustic features for speaker verification. To reduce the high dimensionality required for training the feature vectors, we use a dimension reduction method called Principal Component Analysis (PCA) in front-end step. Performance evaluations are conducted using the AURORA database and the robustness of the performed systems was evaluated under different noisy environments. The experimental results show that PCA dimensionality reduction improves significantly the recognition accuracy in speaker verification task, especially in noisy environments.