A vehicle is a product that encloses high levels of complexity. Assessing its quality requires taking into account several variables simultaneously. Usually, this kind of analysis is made over one variable at a time, ignoring the multidimensional nature of the quality. This is even more critical when two or more vehicles are included in the analysis (e.g. for benchmarking purposes), or when the aim of the analysis is to evaluate the performance of more than one variable over time. This study presents an overview of the biplot, which is a low-dimensional representation of observations and variables, and the possibility to use it in monitoring multiple quality variables. We show a case study demonstrating that Principal Components Analysis (PCA) allows us to summarize in a two-dimensional biplot the information that would require a correlation matrix, several conventional plots and further analysis when comparing eight variables measured on two vehicles over the last four years. This paper is intended to highlight the potential value of this methodology as a quality tool in the automotive industry.