This paper presents a data-driven decision-theoretic approach to making grounding decisions in spoken dialogue systems, i.e., to decide which recognition hypotheses to consider as correct and which grounding action to take. Based on task analysis of the dialogue domain, cost functions are derived, which take dialogue efficiency, consequence of task failure and information gain into account. Dialogue data is then used to estimate speech recognition confidence thresholds that are dependent on the dialogue context.