Activity recognition (AR) or in other words context recognition is an active area of research in the domain of pervasive and mobile computing that has direct applications about life quality and health of the users. Related studies aim to classify different daily human activities with high accuracy rates using various types of sensors. Becoming a substantial part in our daily lives with their sensing capabilities, smartphones are now feasible platforms that enable people to make use of AR technologies without being obliged to use or wear some extra devices. However, due to the fact that users carry these devices at different positions, such as in the pocket or in the bag, it becomes a challenging task to attain accurate results by directly used classification models. In this paper, we focus on phone position uncertainty problem and compare the classification results with position independent and position dependent classification models.