The support vector regression （SVR） approach combined with particle swarm optimization for parameter optimization, is proposed to establish a model for estimating the density of selective laser sintering parts under processing parameters, including layer thickness, hatch spacing, laser power, scanning speed, ambient temperature, interval time and scanning mode. A comparison between the prediction results and the results from the BP neural networks strongly supports that the internal fitting capacity and prediction accuracy of SVR model are superior to those of BP neural networks under the identical training and test samples; the generation ability of SVR model can be efficiently improved by increasing the number of training samples. The minimum error value is provided by leave-one-out cross validation test of SVR. These results suggest that SVR is an effective and powerful tool for estimating the density of selective laser sintering parts.