In this paper a new method for on-line signature authentication will be presented, which is based on a event-string modelling of features derived from pen-position and pressure signals of digitizer tablets. A distance measure well known from textual pattern recognition, the Levenshtein Distance, is used for comparison of signatures and classification is carried out applying a nearest neighbor classifier. Results from a test set of 1376 signatures from 41 persons are presented, which have been conducted for four different feature sets. The results are rather encouraging, with correct identification rates of 96% at zero false classifications.