Automatic structuring (feature coding and object recognition) of topographic data, such as that derived from air survey or raster scanning large-scale paper maps, requires the classification of objects such as buildings, roads, rivers, fields and railways. The recognition of objects in computer vision is largely based on the matching of descriptions of shapes. Fourier descriptors, moment invariants, boundary chain coding and scalar descriptors are methods that have been widely used and have been developed to describe shape irrespective of position, orientation and scale. The applicability of the above four methods to topographic shapes is described and their usefulness evaluated. All methods derive descriptors consisting of a small number of real values from the object's polygonal boundary. Two large corpora representing data sets from Ordnance Survey maps of Purbeck and Plymouth were available. The effectiveness of each description technique was evaluated by using one corpus as a training-set to derive distributions for the values for supervised learning. This was then used to reclassify the objects in both data sets using each individual descriptor to evaluate their effectiveness. No individual descriptor or method produced consistent correct classification. Various models for the fusion of the classification results from individual descriptors were implemented. These were used to experiment with different combinations of descriptors in order to improve results. Overall results show that Moment Invariants fused with the minfusion rule gave the best performance with the two data sets. Much further work remains to be done as enumerated in the concluding section.