Increasing number of vehicles are equipped with telematics devices and are able to transmit vehicle CAN bus information remotely. This paper examines the possibility of identifying individual drivers from their driving signatures embedded in these telematics data. The vehicle telematics data used in this study were collected from a small fleet of 30 Ford Fiesta vehicles driven by 30 volunteer drivers over 15 days of real-world driving in London, UK. The collected CAN signals included vehicle speed, accelerator pedal position, brake pedal pressure, steering wheel angle, gear position, and engine RPM. These signals were collected at approximately 5Hz frequency and transmitted to the cloud for offline driver identification modeling. A list of driving metrics was developed to quantify driver behaviors, such as mean brake pedal pressure and longitudinal jerk. Random Forest (RF) was used to predict driver IDs based on the developed driving metrics. The RF model was also used to rank the importance of each driving metric on driver identification. In conclusion, this paper demonstrated the possibility of identifying drivers from their on-road naturalistic driving behaviors with 100% accuracy within 6 minutes of driving by training the RF model with 4 hours of driving data.