The main problem to identify skilled forgeries for offline signature verification lies in the fact that it is difficult to formalize distinguished feature representation of the signature patterns and design appropriate fusion scheme for various types of feature vectors. To tackle these problems, in this paper, we propose an approach to extract robust Edge Orientation Distance Histogram (EODH) descriptor which effectively reflects signature structure variations. In addition, directional gradient density features are employed for skilled forgery verification attempt. To exploit the full capacity of two sets of features, we designed the multilevel weighted fuzzy classifier and fuse match scores by way of selection priority. Experiments were conducted on a subcorpus of open MCYT signature database which is widely used for performance evaluation. It shows that the proposed method was able to improve verification accuracy. © 2009 World Scientific Publishing Company.