We consider personal identification using stroke order variations of online handwritten character patterns, which are written on, e.g., electric tablets. To extract the stroke order variation of an input character pattern, it is necessary to establish the accurate stroke correspondence between the input pattern and the reference pattern of the same category. In this paper we compare five stroke correspondence methods: the individual correspondence decision (ICD), the cube search (CS), the bipartite weighted matching (BWM), the stable marriage (SM), and the deviation-expansion model (DE). After their brief review, they are experimentally compared quantitatively by not only their stroke correspondence accuracy but also character recognition accuracy. The experimental results showed the superiority CS and BWM over ICD, SM and DE.