With the quickly rising popularity of multimedia, especially of the audiovisual data, the need to understand the inner structure of such data is increasing. In this case study, we propose a method for structure discovery in recorded lectures. The method consists in integrating a self-organizing map (SOM) and hierarchical clustering to find a suitable cluster structure of the lectures. The output of every SOM is evaluated by various levels of hierarchical clustering with different number of clusters mapped to the SOM. Within these mapped levels we search for the one with the lowest average within-cluster distance, which we consider the most appropriate number of clusters for the map. In experiments, we applied the proposed approach, with SOMs of four different sizes, to nearly 16 000 slides extracted from the recorded lectures.