A novel offline signature modeling is introduced and evaluated which attempts to advance a grid based feature extraction method uniting it with the use of an ordered powerset. Specifically, this work represents the pixel distribution of the signature trace by modeling specific predetermined paths having Chebyshev distance of two, as being members of alphabet subsets-events. In addition, it is proposed here that these events, partitioned in groups, are further explored and processed within an ordered set context. As a proof of concept, this study progresses by counting the events’ first order appearance (in respect to inclusion) at a specific powerset, along with their corresponding distribution. These are considered to be the features which will be employed in a signature verification problem. The verification strategy relies on a support vector machine based classifier and the equal error rate figure. Using the new scheme verification results were derived for both the GPDS300 and a proprietary data set, while the proposed technique proved quite efficient in the handling of skilled forgeries as well.