A signature verification method that combines recognition methods of one-dimensional signals, e.g., speech and on-line handwriting, and two-dimensional images, e.g., holistic word recognition in OCR and off-line handwriting is described. In the one-dimensional approach, a sequence of data is obtained by tracing the exterior contour of the signature which allows the application of string-matching algorithms. The upper and lower contours of the signature are first determined by ignoring small gaps between signature components. The contours are combined into a single sequence so as to define a pseudo-writing path. To match two signatures a non-linear normalization method, viz., dynamic time warping, is applied to segment them into curves. Shape descriptors based on Zernike moments are extracted as features from each segment. A harmonic distance is used for measuring signature similarity. The two-dimensional approach is based on using features describing the word-shape. When the two methods are combined, the overall performance is significantly better than either method alone. With a database of 1320 genuines and 1320 forgeries the combination method has an accuracy of 90%.