Structure distortion evaluation allows us to directly measure the similarity between signature patterns without classification using feature vectors, which usually suffers from limited training samples. In this paper, we incorporate the merits of both global and local alignment algorithms to define structure distortion using signature skeletons identified by a robust wavelet thinning technique. A weak affine model is employed to globally register two signature skeletons and structure distortion between two signature patterns, which are determined by applying an elastic local alignment algorithm. Similarity measurement is evaluated in the form of Euclidean distance of all found corresponding feature points. Experimental results showed that the proposed similarity measurement was able to provide sufficient discriminatory information in terms of equal error rate being 18.6% with four training samples. © 2007 World Scientific Publishing Company.