Speaker identification systems are critical components of various applications, including security, authentication, and voice-controlled devices. However, their performance can be affected by environmental noise, channel distortions, and speaker variability. This paper presents an enhanced speaker identification system using two-stage vector quantization to improve robustness against such challenges. The proposed system employs a two-stage approach: first, the input speech features are quantized using vector quantization to reduce dimensionality and enhance discriminability. Then, a classifier is trained on the quantized feature vectors to perform speaker identification. Experimental results demonstrate that the two-stage vector quantization approach significantly improves the robustness of the speaker identification system, achieving higher accuracy even in noisy and adverse conditions.