Multivariate statistical process control MSPC.is applied to an electrolysis process. The process produces extremely pure copper, and to monitor its quality the levels of eight metal impurities were recorded twice a day. These quality data are analysed adopting an 1. ‘intuitive’ univariate approach, and 2. with multivariate techniques. It is demonstrated that the univariate analysis gives confusing results with regards to outlier detection, while the multivariate approach identifies two types of outliers. Moreover, it is shown how the results from the multivariate principal component analysis PCA.method can be displayed graphically in multivariate control charts. Multivariate Shewhart, cumulative sum CUSUM.and exponentially weighted moving average EWMA.control charts are used and compared. Also, an informationally powerful control chart, the simultaneous scores monitoring and residual tracking SMART.chart, is introduced and used.