Hydrometallurgy is a popular metallurgical technology. Filter press is common but vital to the production of hydrometallurgy. Hence, the process monitoring of filter press is of great significance for hydrometallurgy. Due to data analysis and related knowledge of filter press, Principal component analysis (PCA) is applied to process monitoring of the filter press via two traditional statistics. However, modeling and test data collected from actual production suffers from outliers, missing data, inconsistent sampling period between variables. Based on these practical problems, corresponding data proceeding technique is proposed. The final application simulation illustrates the validity of the proposed method.