State-of-the-art intelligent assistant systems such as Siri & Co. struggle with conditionals. They reliably react to ordinary commands. However, their architectures are not designed to cope with complex conditional queries. We propose a system to overcome these limitations. Our approach models if-then-else constructs in spoken utterances explicitly. The model bridges the gap between linguistic and programmatic semantics. To proof our concept, we apply a rule-based approach to extract conditionals. For our prototype we use part-of-speech and chunk tags provided by NLP tools. We make use of coreference information to determine the reference frame of a condition. The explicit modeling of conditionals allows us to evaluate the accuracy of our approach independently from other language understanding tasks. The prototype works well in the domain of humanoid robotics. In a user study we achieve F1 scores of 0.783 (automatic speech recognition) up to 0.898 (manual transcripts) on unrestricted utterances.