AbstractThe wide propagation of devices, such as mobile phones, that include a global positioning system (GPS) sensor has popularised the storing of geographic information for different kinds of activities, many of them recreational, such as sport. Extracting and learning knowledge from GPS data can provide useful geographic information that can be used for the design of novel applications. In this paper we address the problem of identifying the sport from a GPS track that is recorded during a sport session. For that purpose, we store 8500 GPS tracks from ten different kinds of sports. We extract twelve features that are able to represent the activity that was recorded in a GPS track. From these features several models are induced by diverse machine learning classification techniques. We study the problem from two different perspectives: flat classification, i.e, models classify the track in one of the ten possible sport types; and hierarchical classification, i.e. given the high number of classes and the structure of the problem, we induce a hierarchy in the classes and we address the problem as a hierarchical classification problem. For this second framework, we analyse three different approaches. According to our results, multiclassifier systems based on decision trees obtain the better performance in both scenarios.