In order to increase the predictive precision of gaussian process regression based soft sensor, a composite gaussian process regression model is proposed. This model combines the outputs of several gaussian process models as the output according to the variances and the distribution of the outputs, which results in higher prediction accuracy and higher robustness than the single gaussian process model. The proposed composite gaussian process regression model is successfully applied to the prediction of silicon content in hot metal.