There are about as many strategies for listening to music as there are music enthusiasts. This makes learning about overarching patterns and similarities difficult. In this paper, we present an empirical analysis of long-term music listening histories from the last.fm web service. It gives insight into the most distinguishing factors in music listening behavior. Our sample contains 310 histories with up to six years duration and 48 associated variables describing various user and music characteristics. Using a principal components analysis, we aggregated these variables into 13 components and found several correlations between them. The analysis especially showed the impact of seasons and a listener's interest in novelty on music choice. Using this information, a sample of a user's listening history or even just demographical data could be used to create personalized interfaces and novel recommendation strategies. We close with derived design considerations for future music interfaces.