Soft sensor is widely used in industrial process control. It plays an important role to improve the quality of product and assure safety in production. The core of soft sensor is to construct soft sensing model. A new soft sensing modeling method based on support vector machine (SVM) is proposed. SVM is a new machine learning method based on statistical learning theory and is powerful for the problem characterized by small sample, nonlinearity, high dimension and local minima. The proposed methods are applied to the estimation of frozen point of light diesel oil in distillation column. The estimated outputs of soft sensing model based on SVM match the real values of frozen point and follow varying trend of frozen point very well. Experiment results show that SVM provides a new effective method for soft sensing modeling and has promising application in industrial process applications. It plays an important role to improve the quality of product and assure safety in production. The core of soft sensor is to construct soft sensing model. A new soft sensing modeling method based on support vector machine (SVM) is proposed. SVM is a new machine learning method based on statistical learning theory and is powerful for the solution characterized by small sample, nonlinearity, high dimension and local minima. The proposed methods are applied to the estimation of frozen point of light diesel oil in distillation column. The estimated outputs of soft sensing model based on SVM match the real values ​​of frozen point and follow varying trend of frozen point very well. Experiment results show that SVM provides a new effective method for soft sensing modeling and has promising application in industrial process applications.