Skilled musicians are able to shape a given piece of music (by continuously modulating aspects like tempo, loudness, etc.) to communicate high level information such as musical structure and emotion. This activity is commonly referred to as expressive music performance. The present paper presents another step towards the automatic high-level analysis of this elusive phenomenon with AI methods. A system is presented that is able to measure tempo and dynamics of a musical performance and to track their development over time. The system accepts raw audio input, tracks tempo and dynamics changes in real time, and displays the development of these expressive parameters in an intuitive and aesthetically appealing graphical format which provides insight into the expressive patterns applied by skilled artists. The paper describes the tempo tracking algorithm (based on a new clustering method) in detail, and then presents an application of the system to the analysis of performances by different pianists.