A quantitative structure-mobility relationship (QSMR) was developed for the absolute mobilities of 115 carboxylic and sulphonic acids in capillary electrophoresis based on the descriptors calculated from the structure alone. The heuristic method (HM) and radial basis function neural networks (RBFNN) were utilized to construct the linear and nonlinear prediction models, respectively. The prediction results were in agreement with the experimental values. The HM model gave an root-mean-square (RMS) error of 3.76 electrophoretic mobility units for the training set, 5.59 for the test set, and 4.19 for the whole data set, while the RBFNN gave an RMS error of 1.78, 2.04, and 1.83, respectively. The heuristic linear model could give some insights into the factors that are likely to govern the mobilities of the compounds, however, the prediction results of the RBFNN model seem to be better than that of the heuristic method.