Prediction of road slope is a key technology to vehicles’ electronic real-time control system, such as ABS, AMT and hybrid torque distribution, and so on. In this paper, a real-time prediction method of road slope was put forward based on support vector machine (SVM), in which the input parameters of SVM module included engine speed, engine output torque, vehicle speed and longitudinal acceleration, and could be extracted from controller CAN network in real-time. The vehicle roadway test system and the CarSim simulation platform were built up respectively, and the samples required for SVM model learning, generalization performance test were achieved by the systematic tests. The squared correlation coefficient of SVM model from CarSim tests was 0.99, while it was 0.9 from roadway tests. The main reason for the difference could be that the GPS method in road slope test may add in a body pitch angle which could not be eliminated systematically. Furthermore, the SVM model of roadway test was imported into the real time virtual controller PXIe using LabVIEW programming method. For the equivalent prediction time of one point to the single chip computer selected by automotive electronic controller was only 1.33ms , which met the requirements of real time control . The road slope prediction method proposed in this paper is effective and practicable.