As all drivers tend to have their own habitual choice of driving manoeuvre, causing variations in fuel consumption, it would hence be beneficial to classify these driving styles and extract the most economical and ecological driving patterns. However, it should be noted that driving style of each driver is not consistent and may vary within a single trip. Therefore, this paper proposes a Support Vector Clustering based driving style classification approach, which attempts to differentiate the variations in individual’s driving pattern and provides a more objective driver classification. It can potentially be used in developing more economical and personalized advanced driver assistance systems (ADAS) and autonomous driving strategies. With the easily accessible on-board diagnostics (OBD) data on modern vehicles, driving data of three drivers were collected using an instrumented Volkswagen Sharan. Alongside with the vehicle state information, such as vehicle speed, engine speed and throttle pedal position, the headway distance to the leading vehicle was also collected using a Continental radar and a monocular dashcam. Each trip data was synchronized, and segmented into separate event groups, such as accelerating, braking, maintaining and completely stop, using a dynamic sliding window approach. Afterwards, Discrete Wavelet Transform (DWT) was adopted to compute the spectral features of each signal. Moreover, prominent factors were extracted by applying Principal Component Analysis (PCA) on the combination of all statistical and spectral features. Finally, Support Vector Clustering (SVC) was performed within each event group to classify driving patterns during the trip. The obtained results were assembled to indicate the driving pattern variations. The driving style was hence classified in percentage and the fuel consumption variations caused by different driving patterns were evaluated. Moreover, the influence of weather conditions on driving style variation was also investigated. Furthermore, three drivers were also compared to identify the most economical driving patterns.