The recent increasing availability of fine-grained electrical consumption data allows the exploitation of Pattern Recognition techniques to characterize and analyse the behaviour of energy customers. The Pattern Recognition analysis is typically performed at group level, i.e. with the aim of discovering, via clustering techniques, groups of users with a coherent behaviour – this being useful, for example, for targeted pricing or collective energy purchasing. In this paper we took a step forward along this direction, investigating the possibility of discriminating the behaviours of single users – i.e., in a biometrics sense. This aspect has not been properly addressed and would pave the way to crucial operations, such as the derivation of alternative advertising schemes based on behavioural targeting. To investigate the uniqueness of the load profiles (i.e. the daily consumption of electrical energy), in our study we used the raw data (the original energy consumption time series) as well as different types of features such as frequency coefficients and normalized load shape indexes, together with various classification schemes. Results obtained on two real world datasets suggest that the load profile does contain significant distinctive information about the single user.