One approach to object recognition is the -matching of two-dimensional contours, which are obtained from the projection of a three-dimensional model, with aggregates of lines extracted from an image. It is necessary to define geometric shape features which aid in the matching and can be used to compute a confidence measure for the match. Some of the standard features include curvature maxima and minima, points of inflection, trihedral vertices, and T-junctions. There has not been much evidence that global transforms such as Fourier series or symmetric axis transform make the solution any easier. What is needed is a hierarchical description which includes smooth curve segments and the types of junctions between them. A geometric grouping process is described which might be able to produce symbolic tokens in an image which could be matched hierarchically with a description from the model.