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Given the size of todays professional image databases, the stan-dard approach to object- or theme-related image retrieval is to in-teractively navigate through the content. But as most users of such databases are designers or artists who do not have a technical back-ground, navigation interfaces must be intuitive to use and easy to learn. This paper reports on efforts towards this goal. We present a system for intuitive image retrieval that features different moda-lities for interaction. Apart from conventional input devices like mouse or keyboard it is also possible to use speech or haptic gesture to indicate what kind of images one is looking for. Seeing a selection of images on the screen, the user provides relevance feedback to narrow the choice of motifs presented next. This is done either by scoring whole images or by choosing cer-tain image regions. In order to derive consistent reactions from multimodal user input, asynchronous integration of modalities and probabilistic reasoning based on Bayesian networks are applied. After addressing technical details, we will discuss a series of usability experiments, which we conducted to examine the impact of multimodal input facilities on interactive image retrieval. The results indicate that users appreciate multimodality. While we ob-served little decrease in task performance, measures of contentment exceeded those for conventional input devices.