Automatic shot boundary detection has been an active research area for nearly a decade and has led to high performance detection algorithms for hard cuts, fades and wipes. Reliable dissolve detection, however, is still an unsolved problem. In this paper, we present the first robust and reliable dissolve detection system. A detection rate of 69 percent was achieved while reducing the false alarm rate to an acceptable level of 68 percent on a test video set for which so far the best reported detection and false alarm rate had been 57 percent and 185 percent, respectively. In addition, the temporal extent of the dissolves are estimated by a multi-resolution detection approach. The three core ideas of our novel approach are firstly the creation of a dissolve synthesizer capable of creating in principle an infinite number of dissolve examples of any duration form a video database of raw video footage, secondly tow new features for capturing the characteristics of dissolves, and thirdly, the exploitation of machine learning ideas for reliable object detection such as the boostrap-method to improve the set of non-dissolve examples and the search at multiple resolutions as well as the usage of machine learning algorithms such as neural networks, support-vector machines and linear vector quantizer.