Titanium alloys are widely used in various fields，the processing quality of this materials will be affected by the milling force and deformation easily caused, so that the milling force should be predicted. Pointing on the problem that the milling force function not explicitly expressed in practical machining process, a prediction algorithm is proposed for milling force based on support vector machine (SVM). The milling force prediction model was established by using the orthogonal experiment method to select suitable design parameters sample, the prediction and experimental fitting curves were worked out, experimental values were obtained by using Finite Element Modeling (FEM), and the error rate and its significant testing analysis of the predicted values and the experimental results were made respectively. In order to test the effectiveness of the proposed algorithm of SVM, back progation neural network (BPNN) was also used to establish the predict model for experimental valuse. Prediction results show that the predication results are more accurate by using SVM, comparing with the BPNN.