A method for the automatic verification of on-line handwritten signatures using both global and local features is described. The global and local features capture various aspects of signature shape and dynamics of signature production. We demonstrate that with the addition to the global features of a local feature based on the signaturelikelihood obtained from Hidden Markov Models (HMM), the performance of signature verification improves significantly. The current version of the program has 2.5% equal error rate. At the 1% false rejection (FR) point, the addition of the local information to the algorithm with only global features reduced the false acceptance (FA) rate from 13% to 5%.