This paper describes Brno University of Technology (BUT) system for the Interspeech 2009 Emotion Challenge. Our submitted system for the Open Performance Sub-Challenge uses acoustic frame based features as a front-end and Gaussian Mixture Models as a back-end. Different feature types and modeling approaches successfully applied in speaker- and language recognition are investigated and we can achieve an 16% and 9% relative improvement over the best dynamic and static baseline system on the 5-class task, respectively.