This paper introduces a centroid-based (CB) supervised classification algorithm of textured images. In the context of scale/orientation decomposition, we demonstrate the possibility to develop centroid approach based on multivariate stochastic modeling. The main interest of the multivariate modeling comparatively to the univariate case is to consider spatial dependency as additional features for characterizing texture content. The aim of this paper is twofold. Firstly, we introduce the Spherically Invariant Random Vector (SIRV) representation for the modeling of wavelet coefficients. Secondly, from the specific properties of the SIRV process, i.e. the independence between the two sub-processes of the compound model, we derive centroid estimation scheme. Experiments from various conventional texture databases are conducted and demonstrate the interest of the proposed classification algorithm.