Relative positioning between components of a structured object plays a key role for its interpretation. Fuzzy relative positioning templates are a description framework for 2D handwritten patterns, that is based on positioning models specifically designed for dealing with variability and imprecision of handwriting. In this work, we present fuzzy positioning templates and investigate the idea of recognizing structured handwritten symbols by considering the relative positioning of the components, rather than the shapes of the components themselves or the global shape of the symbol. The templates are automatically trained from data without requiring any prior knowledge. Experiments on a database of on-line symbols prove that this original strategy is a promising approach for interpretation of structured patterns.