Tight turns play an important role in globular proteins from both the structural and functional points of view. Of tight turns, β-turns and γ-turns have been extensively studied, but α-turns were little investigated. Recently, a systematic search for α-turns classified α-turns into nine different types according to their backbone trajectory features. In this paper, Support Vector Machines (SVMs), a new machine learning method, is proposed for predicting the α-turn types in proteins. The high rates of correct prediction imply that that the formation of different α-turn types is evidently correlated with the sequence of a pentapeptide, and hence can be approximately predicted based on the sequence information of the pentapeptide alone, although the incorporation of its interaction with the other part of a protein, the so-called ''long distance interaction'', will further improve the prediction quality.