This paper proposes an on-line handwritten character pattern recognition method that examines spatial relationships among subpatterns which are components of a character pattern. Conventional methods evaluating spatial relationships among subpatterns have not considered characteristics of deformed handwritings and evaluate all the spatial relationships equally. However, the deformations of spatial features are different within a character pattern. In our approach, we assume that the distortions of spatial features are dependent on the hierarchy of character patterns so that we selectively evaluate hierarchical spatial relationships of subpatterns by employing Bayesian network as a post-processor of our sub-stroke based HMM recognition system. Experiments on on-line handwritten Kanji character recognition with a lexicon of 1,016 elementary characters revealed that the approach we propose improves the recognition accuracy for different types of deformations.