Abstract: Stochasticity is proposed as a feature for texture characterization and analysis. Measuring stochasticity requires finding suitable representations that can significantly reduce statistical dependencies of any order. Wavelet packet representations provide such a framework for a large class of stochastic processes. The paper first addresses the selection of the best wavelet packet basis with respect to the stochasticity criterion and by using the Kolmogorov stochasticity parameter. A best basis under stochasticity consideration makes possible accurate texture description trough a dictionary of parametric models, especially for non regular textures. Among the properties of such a representation, the paper shows that texture classification is possible through stochasticity consideration. The relevance of the analysis also makes possible content-based stochasticity retrieval with semantics and with respect to the order structure of the wavelet packet bases.