This paper investigates writer verification using handwritten kanji characters on a digitizing tablet. Features representing individuality, which are derived from the knowledge of document examiners, are automatically extracted and then the features effective in writer verification are selected from the extracted features. Two classifiers based on frequency distribution of deviations of the selected features are proposed and evaluated by verification experiments. The experimental results show that the proposal methods are effective in writer verification.