A multi-resolution method for training the LBG vector quantizer and a Kohonen competitive neural network (KCNN) is presented. Starting with a low resolution sample of the input data, the training algorithm is applied to a sequence of monotonically increasing-resolution samples of the data. The final centers (weight matrix) obtained from a low resolution stage are used as the initial centers for the next stage which is a higher resolution stage. The multi-resolution algorithm is tested using synthetic data and several multi-spectral images and compared to the traditional LBG / KCNN. It is found that in the average case the multi-resolution reduces the computation time by a factor of more than four for LBG-VQ and two for KCNN-VQ with a slight improvement in the quality of quantization The validity of the multi-resolution algorithm is assessed through evaluation of the entropy of the results, the entropy of the problem space, and the probability of achieving a global optimum. A discussion on alternative approaches for validation will conclude the presentation.