SVM is a machine learning technique primarily for classification. SVM was initially proposed as a linear classifier, while Vapnik et al. expanded its application as a nonlinear classifier in 1995. It is a mature and reliable method, the success of which was proven in the fields of pattern recognition and computer vision problems. For a typical chemical process, the high-dimensional features (or multiple inputs) and the nonlinearity are unavoidable. Ibrahim has demonstrated its successful application in chemical process engineering. A toolbox of SVM can be accessed in MATLAB, so SVM is selected as the classifier in this work.