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In this paper we address the problem of hand-drawn symbol spotting in document images. We use stochastic graphical models (SGMs) to represent the structure and variations of hand-drawn symbols. We use a framework which first carries out segmentation and graph formation of the input image, followed by sub-graph matching for spotting of hand-drawn symbols. We used SGMs in place of sub-graphs in a semi-definite programming based sub-graph matching to do the spotting. The experimental results validate our framework. We were able to spot hand-drawn symbols from 10 classes with 78.89% accuracy in a database of 76 document images and also were able to deal with confusingly similar symbol classes.