Abstract: A support vector data description (SVDD) was proposed to be introduced in the monitoring, diagnosis and optimization of processes. Firstly, the SVDD monitor model was established to obtain the control limit based on normal samples. Then, the contribution chart was used to diagnose outliners exceeding the control limit in statistics to find the main causes of abnormal production. Finally, the process parameter optimization was performed by the adjacent point replacement. The proposed method was applied to the process of cold rolled sheets. The results show that this method has a higher detection rate than traditional T^{2 PCA} during the production process monitoring, and can optimize the process parameters to make it return to the controlled state.