Due to the increasing vulnerabilities in cyberspace, security alone is not enough to prevent a breach, but cyber forensics or cyber intelligence is also required to prevent future attacks or to identify the potential attacker. The unobtrusive and covert nature of biometric data collection of keystroke dynamics has a high potential for use in cyber forensics or cyber intelligence. In this paper, we investigate the usefulness of keystroke dynamics to establish the person identity. We propose three schemes for identifying a person when typing on a keyboard. We use various machine learning algorithms in combination with the proposed pairwise user coupling technique and show the performance of each separate technique as well as the performance when combining two or more together. In particular, we show that pairwise user coupling in a bottom-up tree structure scheme gives the best performance, both concerning accuracy and time complexity. The proposed techniques are validated by using keystroke data. However, these techniques could equally well be applied to other pattern identification problems. We have also investigated the optimized feature set for person identification by using keystroke dynamics. Finally, we also examined the performance of the identification system when a user, unlike his normal behaviour, types with only one hand, and we show that performance then is not optimal, as was to be expected.