This paper deals with three different methodsfor grasp recognition for a human hand. Grasp recognitionis a major part of the approach for Programming-by-Demonstration (PbD) for five-fingered robotic hands. A humanoperator instructs the robot to perform different grasps wearinga data glove. For a number of human grasps, the finger jointangle trajectories are recorded and modeled by fuzzy clusteringand Takagi-Sugeno modeling. This leads to grasp models usingthe time as input parameter and the joint angles as outputs.Given a test grasp by the human operator the robot classifiesand recognizes the grasp and generates the corresponding robotgrasp. Three methods for grasp recognition are presented andcompared. In the first method the test grasp is comparedwith model grasps using the difference between the modeloutputs. In the second one, qualitative fuzzy models are usedfor recognition and classification. The third method is based onHidden-Markov-Models (HMM) which are commonly used inrobot learning