Abstract: A novel NGPP-SVDD statistical process monitoring approach is proposed to address non-Gaussian multivariate process systems. Such systems present typical challenges to traditional multivariate process monitoring approaches. The particle swarm optimization (PSO) based NGPP approach is proposed to extract the most non-Gaussian component out of the process records and overcome the deficiency of traditional FastICA (fast independent component analysis) algorithm, which is easily trapped in local minimum. Criteria to automatically select the number of non-Gaussian components are also given. Transforming the exacted non-Gaussian components into a feature space using a support vector data description then allows the application of a simple parametric statistical inference. Numerical study and industrial melting process application show the efficiency of the proposed method.