The problem of model-based object recognition is considered as a computational process incorporating a means of clustering feature data consistent with the parts of a structural shape model. A general approach is developed that using both continuity and shape constraints for fitting axial-curve models to derived feature patterns. This integrated approach allows for noise datums to be disregarded, while missing data can be inferred by the interpretation of axial point sequences. Complete object structures are recovered using a circular operator to detect features of shape discontinuity (corners, junctions and tips). The approach is demonstrated on images from various domains, with the main result being a suburban road network analysis of a high resolution aerial image. Other results include overlapping circles, circuit board traces, and a LANDSAT image of the Mississippi river.