A$ Virtual Reality (VR) becomes increasingly popular and affordable, and is applied in other fields than entertainment, such as education and industrial use, there is also a grow­ing risk related to its integrity and security. VR equipment tracks user biometric data as a means to interact with the VR environment, which creates sets of biometric data that could be used to identify u ers. Such biometric tempJates are potentially harmful if stolen by a malicious third party. This thesis investigates if user identification is possible within a set of participants ( =10) through a study using their movement and eye biometric data gathered within VR sessions, where they perform a teleoperation task designed to sim­ulate a real-world use case. By performing 3 data collection sessions for each participant and using the gathered data to train 4 classification models, we show that a high level of accuracy can be attained while using simple machine learning approaches, achieving a peak accuracy of 89.26% with a data5et designed to challenge our models. We further ana­lyze the accuracy results from the trained models, and di5cuss the identification power of different data types, which highlights how the characteristics of the task performed affects the usefulness of data types.