The ability to relate a digital photograph to its source camera has application in the areas of digital forensics and multimedia data mining. The majority of previous research in this area has focused on primary function imaging devices (i.e. digital cameras). In this work we use the pattern noise of an imaging sensor to classify digital photographs according to the source smartphone from which they originated. This is timely work as new smartphone models large imaging sensors, afford significant improvements in classification rates using pattern noise. Our approach is to extract wavelet based features which are then classified using a support vector machine. We show that this method generalises well when the number of source cameras is increased.