This research focuses on real-time gesture learning and recognition. Events arrive in a continuous stream without explicitly given boundaries. To obtain temporal accuracy, weneed to consider the lag between the detection of an eventand any effects we wish to trigger with it. Two methodsfor real time gesture recognition using a Nintendo Wii controller are presented. The first detects gestures similar to agiven template using either a Euclidean distance or a cosinesimilarity measure. The second method uses novel information theoretic methods to detect and categorize gestures inan unsupervised way. The role of supervision, detection lagand the importance of haptic feedback are discussed.