In table tennis, the rotation (spin) of the ball plays a crucial role. A table tennis match will feature a variety of strokes. Each generates different amounts and types of spin. To develop a robot that can compete with a human player, we need detect the spin, so the robot system can plan an appropriate return stroke. We propose three different methods to estimate spin [2]. For the first two approaches, we use a high-speed camera that captures the ball in flight at a frame rate of 380 Hz. This camera allows the movement of the circular brand logo printed on the ball to be seen (see Figure 1). The first approach uses background difference to determine the position of the logo. In a second alternative, we train a CNN to predict the orientation of the logo. The third method evaluates the trajectory of the ball and derives the rotation from the effect of the Magnus force. This method gives the highest accuracy and is used for the demonstration seen below. Our robot successfully copes with different spin types in a real table tennis rally against a human opponent.