This work explores automatic object recognition and semantic capture in vector graphics through shape description. The low-level graphical content of graphical documents, such as a map or architectural drawing, are often captured manually and the encoding of the semantic content seen as an extension of this. The large quantity of new and archived graphical data available on paper makes automatic structuring of such graphical data desirable. Contour shape description techniques, such as Fourier descriptors, moment invariants play an important role in systems for object recognition and representation. However, most work carried out in this area has concentrated on categories of object boundaries representing very specific shapes (for example, a particular type of aircraft). Two classifiers were implemented and proved accurate in their automatic recognition of objects from drawings in different domains. Classical classifier combination techniques were used to improve performance. Further work will employ more complex fusion techniques and it is envisaged they will be used in combination with recognition based on object context using various modelling methods. A demonstration system has been constructed using all these techniques.