User satisfaction is an important aspect of human-computer interaction (HCI) - if a user is not satisfed, he or she might not be willing to use such a system. Therefore, it is crucial to HCI applications to be able to recognise the user satisfaction level in order to react in an appropriate way. For such recognition tasks, data-driven methods have proven to deliver useful and robust results. But a data-driven user satisfaction model needs labelled and reliable data, which is not always easy in the case of user satisfaction, since it is not accessible directly. In the investigation presented here, the users are asked directly about their satisfaction level regarding their performance in a task during a close-to-real-life HCI. This results in a one-to-one mapping between a satisfaction level and the expressed vocal characteristics in the user's utterance. This data is then used to build a model to recognise satised and dissatised user states - as a first step towards a general model of the user's satisfaction state.