We explore the feasibility of identifying users from the unique patterns they exhibit when interacting with an individual electrical appliance in the home. We evaluate the effectiveness of a supervised learning based approach for user identification from a dataset of appliance usage collected across five users and three kitchen appliances over a period of eight weeks. Our results show that using appliance usage information alone provides a moderate average accuracy of 32% for group sizes of up to five users in the home. However augmenting usage information with hints about user presence can improve accuracy by 15-20%.