The implementation of an intermediary proxy is a common approach to the problem of network heterogeneity in the Internet infrastructure. Due to the hypertext nature of the most popular Internet application -the World Wide Web, image compression is considered one of the fundamental functions of such a proxy. It has been observed that most images embedded into Web documents are of 'information-delivery' type, so an algorithm intended for their compression has to satisfy some specific requirements. First, in order to support network (bandwidth) constraints for an arbitrary case, the algorithm should be inherently adaptive, i.e. able to provide a wide range of compression rates. Second, as dealing with images that are integral parts of an interactive application (such as a Web browser), the algorithm should be capable of preserving a sufficient level of image semantics according to the quality standards of human perception. Vector quantization (VQ) technique, in its general form, is proven to satisfy the first requirement. On the other hand, a modified adaptive resonance (modified ART2) learning algorithm (which we employ in this paper) more properly belongs to the family of NN algorithms whose main goal is the discovery of input data clusters, without considering their actual size. This feature makes the modified ART2 algorithm satisfy the second requirement. Thus, the discussion and results presented in this paper are intended to show that modified ART2 underlying the general VQ procedure is an appropriate technique for image compression purposes in a bandwidth-constrained environment.