Temporomandibular disorders (TMD) affect about 5-12 percentage of individuals with consequences such as jaw noises, clicking, myofascial pain, discomfort, limited mandibular range of motion and stress. Treatments depend on the cause and extent of the damage and part of the joint or jaw affected. When exact aetiology of TMD is unclear, generic treatments (splint therapy) are offered. Different oral activities performed on a daily-basis result in different loading conditions on the joint, possibly triggering TMD. These need to be investigated to know the usage of the masticatory system and the potential damage, in order to perform specific treatments. Our work aims at developing an online algorithm that can classify oral tasks performed by individuals. It can be used during daytime or overnight’s sleep to see how often different activities are performed by subjects. A 4 stage wavelet decomposition was employed to the signals and then subjected to feature extraction to train a support vector machine algorithm with. The prediction accuracy was found to be 90 percent for a group of selected oral activities (static, jaw opening, chewing and maximal voluntary clenching). The algorithm had about 80 percent prediction accuracy when classifying both functional (chewing, jaw opening and static) and parafunctional activities (grinding, incisal biting, maximal voluntary clenching, protrusion and laterotrusion) together. However, 80 percent accuracy is regarded as a set back due to the lack of more data. On reviewing the recognised activities, further research on any overuse of muscles or loading on the jaw joint during each activity can be conducted to give specific treatment and therapy preventing any deteriorating actions. Thus,the developed classification algorithm works as a prototype for future studies on online recognition of oral activities.