This paper proposes and compares some approaches for personalizing a handwriting recognizer to a specific user's handwriting style. A typical PocketPC or TabletPC is used by one person exclusively. The handwriting recognizer on such a device can customize its recognition to the specific writing style of the user. This paper presents the results of different personalization approaches for a neural network based classifier, showing how using data specific to the user can dramatically improve recognition accuracy.