The success of Statistical Language Models (SLMS) at improving the performance of Natural Language Processing (NLP) applications suggests their possible applicability to the area of automated map reading. This idea stems from the fact that there are similarities between natural language and cartographic language. We describe a method of using SLM to characterise the context of different classes of objects. We use these models to measure the frequency of each feature context. This can be used to help identify unclassified map features in combination with other methods (for example, based on an object's shape).