Smartphones have become very powerful and personal devices, but still have to live up to their potential. To date, we have no automated means of uncovering a user's task engagement, which would be beneficial in numerous areas -- from mobile applications to human resource management systems. In this thesis, we explore the possibility of automated task engagement inference using smartphone sensors. We try to find an answer by developing a data collection system based on a mobile application. We deploy and distribute the app among volunteers to collect data on our server. We then use machine learning approaches on collected data to uncover a weak link between task engagement and smartphone usage data and find out that the collected data is highly personalized.