User simulation has become an important trend of research in the field of spoken dialogue systems because collecting and annotating real interactions with users is often expensive and time consuming. Yet, such data are generally required for designing and assessing efficient dialogue systems. The general problem of user simulation is thus to produce as many as necessary natural, various and consistent interactions from as few data as possible. In this paper, we propose a user simulation method based on Bayesian Networks (BN) that is able to produce consistent interactions in terms of user goal and dialogue history but also to simulate the grounding process that often appears in human-human interactions. The BN is trained on a database of 1234 human-machine dialogues in the TownInfo domain (a tourist information application). Experiments with a state-of-the-art dialogue system (REALL-DUDE/DIPPER/OAA) have been realized and promising results are presented.