In this work we present a system that uses the accelerometer embedded in a mobile phone to perform activity recognition, with the purpose of continuously and pervasively monitoring the users' level of physical activity in their everyday life. Several classification algorithms are analysed and their performance measured, based for 6 different activities, namely walking, running, climbing stairs, descending stairs, sitting and standing. Feature selection has also been explored in order to minimize computational load, which is one of the main concerns given the restrictions of smartphones in terms of processor capabilities and specially battery life.