A hierarchical random graph (HRG) representation for handwritten character modeling is presented. Based on the HRG, a Hangul, Korean scripts, recognition system also has been developed. In the HRG, the bottom layer is constructed with extended random graphs to describe various strokes, while the next upper layers are constructed with random graphs (Wong and Ghahraman, IEEE Trans. Pattern Anal. Mach. Intell. 2(4) (1980) 341) to model spatial and structural relationships between strokes and between sub-characters. As the proposed HRG is a stochastic model, the recognition is formulated into the problem that chooses a model producing maximum probability given an input data. In this context, a matching score is acquired not by any heuristic similarity function, but by a probabilistic measure. The recognition process starts from converting an input character image into an attributed graph through the preprocessing and the graph representation. Matching between an attributed graph and the hierarchical graph model is performed bottom-up. Since the hierarchical structure in an attributed graph is decided after the recognition ends depending on the best interpretation of the graph matching, we can avoid incorrect sub-character segmentation. Model parameters of the hierarchical graph have been estimated automatically from the training data by EM algorithm (Dempster et al., J. Roy. Stat. Soc. 39 (1977) 1) and embedded training. The recognition experiments conducted with unconstrained handwritten Hangul characters show the usefulness and the e!ectiveness of the proposed HRG.