This paper proposes an algorithm for distributed classification, based on a SVM scheme. The contribution of each support vector is approximated by low complexity distributed thresholding over sub-dictionaries, whose union forms a redundant dictionary of atoms that spans the space of the observed signal. Redundant dictionaries allow for sparse representation of the observed signal, hence a good approximation of the support vector contributions, which is moreover robust to noise. The algorithm is applied to distributed image classification, in the context of handwritten digit recognition in a sensor network. The experimental results indicate that the proposed method is capable of achieving the same classification performance as the standard (non distributed) SVM, with an increased resiliency to noise.