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In this paper we propose recognizing logos by using an adaptive model referred to as recursive artificial neural network. Logo images are converted in a structured representation based on contour trees, where symbolic and sub-symbolic information coexist. A contour-tree is constructed by associating a node with an exterior or interior contour extracted from the logo instance. Nodes in the tree are labeled by a feature vector, which describes the contour by means of its perimeter, surrounded area, and a synthetic representation of its curvature plot. The contour-tree representation contains the topological structured information of logo and continuous values pertaining to each contour node. Afterwards, recursive neural networks are used to learn and recognize the logo instances represented by contour-trees. Experimental results are reported on 40 real logos distorted with artificial noise.