Typing rhythms are the rawest form of data stemming from the interaction between users and computers. When properly sampled and analyzed, they may become a useful tool to ascertain personal identity. Unlike other access control systems based on biometric features, keystroke analysis has not led to techniques providing an acceptable level of accuracy. The reason is probably the intrinsic variability of typing dynamics, versus other, very stable, biometric characteristics, such as face or fingerprint patterns. In this paper, the authors examine an emerging non-static biometric technique that aims to identify users based on analyzing habitual rhythm patterns in the way they type.