This paper explores automatic recognition and semantic capture in vector graphics for graphical information systems. 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. A successful method for recognising text data uses statistical language models. This work will investigate and evaluate similar and adapted statistical models (Statistical Graphical Langauge Models, SGLM) to graphical languages based on the associations between different classes of object in a drawing to automate the structuring and recognition of graphical data.