In this paper we introduce a novel fuzzy vector quantization algorithm that tries to solve certain problems related to the implementation of fuzzy cluster analysis in vector quantization. The proposed method employs an objective function that combines the merits of fuzzy and crisp clustering in a uniform fashion. The algorithm's structure encompasses two basic design strategies. The first one concerns the transition from fuzzy mode, where each training vector is assigned to more than one codewords, to crisp mode where each training vector is assigned to only one codeword. To accomplish this, we use analytical conditions that are extracted by the minimization of the aforementioned objective function. The second one is a specially designed pattern reduction module that helps to significantly reduce the computational cost. This module acts upon a training vector as soon as it is transferred in crisp mode. The resulting vector quantization scheme is fast and easy to implement. Finally, simulation experiments show that the method is efficient, while it appears to be insensitive with respect to the selection of its design parameters.