This paper proposes a new type of a support vector machine which uses a kernel constituted from fuzzy basis functions. The proposed network combines the characteristics both of a support vector machine and a fuzzy system: high generalization performance, even when the dimension of the input space is very high, structured and numerical representation of knowledge and ability to extract linguistic fuzzy rules, in order to bridge the "semantic gap" between the low-level descriptors and the high-level semantics of an image. The Fuzzy SVM network was evaluated using images from the aceMedia Repositoryand more specifically in a beach/urban scenes classification problem.