Trip duration is the most fundamental measure in all modes of transportation. Hence, it is crucial to predict the trip-time precisely for the advancement of Intelligent Transport Systems (ITS) and traveller information systems. In order to predict the trip duration, data mining techniques are employed in this paper to predict the trip duration of rental bikes in Seoul Bike sharing system. The prediction is carried out with the combination of Seoul Bike data and weather data.