We developed a new approach to shape recognition that uses the outline of an object and is based on a novel ???ranch length similarity??(BLS) entropy. ?BLS entropy was defined on a simple branching network consisting of a single node and branches.? The simple pattern was referred to as a ??븂it branching network??(UBN).? The approach involves obtaining BLS entropy profiles from UBNs that are created by joining each pixel in the outline of a shape with every other pixel in the shape???border.? To test whether they can be used effectively to recognize an object???shape, we obtained BLS entropy profiles from the shapes of twenty battle tanks depicted with 460??50 pixel resolution, and identical shapes depicted at lower resolution.? The d-shapes were generated by decreasing the resolution of the original shapes to 60% in 10% increments.? Each BLS entropy profile calculated from the original shape set was compared with all BLS entropy profiles obtained from the d-shapes using correlation coefficients.? The BLS entropy profile for a given shape was more similar to identical shapes depicted at different resolutions than to different shapes.? Therefore, this process was able to successfully discriminate between the shapes tested regardless of resolution, and the proposed approach can be a useful tool for recognizing an object???shape.