Authentication is a process which is used for access control in computer security. However, common existing methods of authentication, which are based on authentication during the login stage, are insecure due to the lack of authentication after the initial instance. Ideally, authentication should be continuous and should not interfere with a user’s normal behavior as to not create an inconvenience for the user. Behaviometric identification, for example, verifies a user’s identity based on his behavior, both continuously and without interruption. This work shows that it is possible, with great accuracy, to identify different users based on their touchpad behaviors. While linear classifiers proved ineffective at classifying touchpad behavior, kernel density estimation and decision tree classification each proved capable of classifying data sets with over 90% accuracy.