Various data-based soft sensor models have been established for online prediction of the silicon content in a pig iron. Actually, modeling data often contain many outliers and this can deteriorate the quality of models. However, little attention is paid to efficient outlier detection. Besides, most of traditional outlier detection methods are assumed that data are distributed (approximately) normally and thus they might be invalid for some situations. A new multivariate preprocessing method for outlier detection without any assumption of data distribution is proposed. This novel outlier detection method mainly utilizes a support vector clustering (SVC) strategy. After SVC-based preprocessing, a support vector regression soft sensor model is built. A comparative study for an industrial blast furnace is investigated and the results show its superiority.