The Laboratory of Pharmaceutical Analytical Chemistry (LCAP) is involved in numerous projects and therefore has to use different analytical instrumentations. Separation techniques, such as chromatography or electrophoresis, remain the techniques of choice but need to be adapted in each specific challenge. To better develop, optimize and validate analytical methodologies, the use of the multivariate approach is of utmost interest. Statistical experimental design is a powerful tool to quantify the effect of one or more variables on a set of measured responses. It provides a methodological framework for changing operating parameters simultaneously by the help of experimental designs. These approaches involve the smallest possible number of useful experiments and provide maximum information. The relevant results obtained in pharmaceutical analysis compared to a univariate approach come from their ability to detect and quantify variable interactions and therefore considerably increase the system knowledge. On the other hand, with the increase of modern computerized analytical techniques, the information could be present as hidden structures in the huge amount of data. As a result, chemometric methods have been applied to extract information. For this purpose, principal component analysis (PCA) combined with other statistical techniques are now necessary to efficiently reduce and interpret complex data structure.