Image compression using the combination of the discrete wavelet transform (DWT) with vector quantization (VQ) has been considered in many recent works. Most of the studies have been dedicated to evaluate the choice of the wavelet filters employed in the multiresolution wavelet image decomposition or to develop bit-allocation schemes. It is worth mentioning, however, that the VQ codebook design plays a crucial role in the quality of the reconstructed image. A competitive neural network algorithm (named SSC) [9] has already been successfully applied for voice waveform VQ codebook design. In the present work, the SSC algorithm is applied in a wavelet/VQ image-coding framework. The SSC codebooks are used to code the image subbands that result from the multiresolution decomposition. The coding results show that the SSC multiresolution codebooks lead to better-reconstructed image quality than that obtained by using JPEG and conventional (spatial domain) VQ.