Gauss mixtur (GM) models are frequently used for their ability to well approximate many densities and for their tractability to analysis. We propose new classification methods built on GM clustering algorithms more often studied and used for vector quantization (VQ). One of our methods is an extension of the 'codebook matching' idea to the specific case of classifying whole images. We apply these methods to a realistic supervised classification problem and empirically evaluate their performances compared with other classification methods.