We investigated in this work a hierarchical fusion strategy for fusing the outputs of hundreds of descriptors~×~classifier combinations. Over one hundred descriptors gathered in the context of the IRIM consortium were used for HLF detection with up to four different classifiers. The produced classification scores are then fused in order to produce a unique classification score for each video shot and HLF. In order to cope with the redundancy of the information obtained from similar descriptors and from different classifiers using them, we propose a hierarchical fusion approach so that 1) each different source type gets an appropriate global weight, 2) all the descriptors~×~classifier combinations from similar source type are first combined in the optimal way before being merged at the next level. The best LIG run has a Mean Inferred Average Precision of 0.1276, which is significantly above TRECVID 2009 HLF detection task median performance. We found that fusion of the classification scores from different classifier types improves the performance and that even with a quite low individual performance, audio descriptors can help.