In this paper we describe a behavioural biometric approach to authenticate users dynamically based on mouse movements only and using regular mouse devices. Unlike most of the previous approaches in this domain, we focus here on the properties of the curves generated from the consecutive mouse positions during typical mouse movements. Our underlying hypothesis is that these curves have enough discriminative information to recognize users. We conducted an experiment to test and validate our model in which ten participants are involved. Back propagation neural network is used as a classifier. Our experimental results show that behavioural information with discriminating features is revealed during normal mouse usage, which can be employed for user modeling for various reasons, such as information assets protection.