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In this paper, we present an online signature verification system based on dynamic time warping (DTW)-based segmentation technique combined with multivariate autoregressive (MVAR) modeling. We also use multilayer perceptron neural network architecture as data classifier. The input data that has been used is (xj,yj) coordinates of signatures drawn from a Persian database. We compare two different DTW algorithms in terms of their effect in improving the alignment between the signature sample and a master signature reference for the subject writer. Our database includes 1250 genuine signatures and 750 forgery signatures that were collected from a population of 50 human subjects. We used 75% of samples for training and 25% for testing. We achieved an accuracy of 88.8% for a skilled forgery test which is a very promising result.