This paper presents a new approach for combining local and global recognition schemes for Automatic Fingerprint Verification (AFV), by using matched local features as the reference axis for generating global features. In our specific implementation, minutia-based and shape-based techniques were combined. The first one matches local features (minutiae) by a point-pattern matching algorithm. The second one generates global features (shape signatures) by using the matched minutiae as its frame of reference. Shape signatures are then digitised to form a feature vector describing the fingerprint. Finally, a LVQ neural network was trained to match the fingerprints by using the difference of a pair of feature vectors. The experimental results show that the integrated system significantly outperforms the minutiae-based system in terms of classification accuracy and stability. This makes the new approach a promising solution for biometric applications.