Vector quantization (VQ) is a efficient technique for data compression with a minimum distortion. VQ is widely used in applications as speech and image coding, speech recognition, and image retrieval. This paper presents a novel fast nearest-neighbor algorithm and shows its application to speech recognition. The proposed algorithm is based on a fast preselection that reduces the search to a limited number of code vectors. The presented results show that the computational cost of the VQ stage can be significantly reduced without affecting the performance of the speech recognizer.