As computing moves beyond the desktop, human activity becomes an essential component of many applications. Activity classification is an active research area and several research systems have been constructed. Most have focused on fragile custom hardware only available in limited quantities. We instead seek to use commodity hardware to lower the barrier to creating activity-informed mobile applications. We describe iLearn, our system for classifying human activities using the Apple iPhone‟s three-axis accelerometer and the Nike+iPod Sport Kit. Our results suggest activities including running, walking, bicycling, and sitting can be recognized at accuracies of 97% without any training by an end-user.