Abstarct: A new online signature verification system baxsed on multivariate autoregressive (MVAR) modeling in combination with a Dynamic Time Warping-baxsed (DTW) segmentation technique is presented in this work. A uniformly spatial-spaced signature sequence is treated as a two element vector sequence (xj,yj). A modified segment-coordinate dynamic time warping algorithm is employed to improve alignment between the signature samples and a master signature reference for the subject writer. Subsequently, a new MVAR model is used to extract coefficients for each segment to construct a feature vector. These vectors are then fed into a Neural Network with multi laxyer perceptron architecture. The performance of the system was evaluated using a testing set of signatures for each writer. These are applied to an on-line signature verification system on signature data from the First International Signature Verification Competition (SVC 2004). The system achieved preliminary accuracies of: 80% in a skill forgery test. And the other data baxse that collected from Iranian's signatures. The system achieved preliminary accuracies on this data baxse: 90% in a skill forgery test.