In multivariate analysis, dimension reduction into a small number of factors is the most important step for capturing the variability among a large number of variables. In JMP, we have the Principal Components (PC) platform to do dimension reduction for continuous variables; however, when we have categorical variables, we cannot model using the PC platform. In JMP 12, we added the Multiple Correspondence Analysis (MCA) platform, which takes multiple categorical variables as input variables and seeks to identify associations between levels of those variables. MCA, a data analysis technique popular in Europe and Japan, is now an addition to our already-robust multivariate toolbox. In this presentation we will use LeRoux et al’s Taste data set combined with survey data collected from our JMP division employees to explore data preparation, statistical analysis, graphical representation and interpretation using the MCA platform. We will also discuss various topics in MCA, such as cloud of categories, cloud of individuals, distances, dimensionality, contributions and supplementary elements to help support the analysis.