This paper presents a comparison of various classification methods for the problem of recognizing grasp types involved in object manipulations performed with a data glove. Conventional wisdom holds that data gloves need calibration in order to obtain accurate results. However, calibration is a time-consuming process, inherently user-specific, and its results are often not perfect. In contrast, the present study aims at evaluating recognition methods that do not require prior calibration of the data glove, by using raw sensor readings as input features and mapping them directly to different categories of hand shapes. An experiment was carried out, where test persons wearing a data glove had to grasp physical objects of different shapes corresponding to the various grasp types of the Schlesinger taxonomy. The collected data was analyzed with 28 classifiers including different types of neural networks, decision trees, Bayes nets, and lazy learners. Each classifier was analyzed in six different settings, representing various application scenarios with differing generalization demands. The results of this work are twofold: (1) We show that a reasonably well to highly reliable recognition of grasp types can be achieved - depending on whether or not the glove user is among those training the classifier - even with uncalibrated data gloves. (2) We identify the best performing classification methods for recognition of various grasp types. To conclude, cumbersome calibration processes before productive usage of data gloves can be spared in many situations.