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In this work a new method for the prediction of the Xaa-proline (where Xaa is any amino acid) cis/trans isomerization was investigated. By extraction of twelve structural features (real secondary structure, inside/outside classification, properties of the environment around proline and proline itself) a support vector machine (SVM) based prediction approach was evolved. The Java software Xaa-PIPT for structural feature extraction was developed. Based on 4397 (2199 cis and 2198 trans) prolines extracted from non-redundant, globular proteins a classifier was trained using the radial basis function (RBF) kernel. In ten-fold cross-validation it achieved an accuracy of 70.0478 % and a Matthews correlation coefficient (MCC) of 0.4223, a sensitivity of 0.5433 and a specificity of 0.8576. Based on this classifier a lightweight and easy-to-use Java software tool, called m Xaa-PIPT, for the prediction of the Xaa-proline cis/trans isomerization was devel-oped. It was shown that there are correlations between the proline surrounding environment and the isomerization state. m Xaa-PIPT can be used for the evaluation of low-resolution protein structures and theoretical models to improve their quality by the prediction of the Xaa-proline isomerization.