We outline how the wavelet transform, a hierarchical averaging scheme, can be used to perform both spatial and topological coarse-graining in systems with multiscale physical behavior, such as Ising lattices and polymer models. We illustrate how to create sampling mechanisms, which we call wavelet-accelerated Monte Carlo (WAMC), to study these systems and obtain qualitatively and quantitatively accurate results in orders of magnitude less time than using atomistic simulations. (c) 2004 Elsevier Ltd. All rights reserved.