Interruptions are an important aspect of human-human communication. They help to adjust the conversation flow. Our aim is to equip virtual agents with the ability to handle interruptions, that is to decide when and how to interrupt their human interlocutor. In this paper, we focus on predicting when interruptions may occur during the conversation using multimodal features only from the speaker and propose a model trained on a corpus of dyadic interactions. To assess the model's accuracy, we conduct a perceptual study where we compare different timings (ground truth, randomly chosen or predicted by our model). CCS CONCEPTS • Human-centered computing → Human agent interaction (HAI).