In this paper, we propose a novel approach of vector quantization using a merge-based hierarchical neural network. Vector quantization¡]VQ¡^is known as a very useful technique for lossy data compression. Recently, Neural network¡]NN¡^algorithms have been used for VQ. Vlajic and Card proposed a modified adaptive resonance theory (modified ART2¡^[1] which is a constructing tree structure clustering method. However, modified ART2 has disadvantages of slow construction rate and constructing many redundant levels. Therefore, we propose a more efficient approach for constructing the tree in this paper. Our method establishes only those required levels without losing the fidelity of a compressed image.