Data sets resulting from sensory and consumer studies can be quite large, with many different columns and data types. A variety of multivariate data analysis methods can be useful in the exploration and analysis of sensory data. Over the past few years, many of these methods have been added to JMP. In this paper, we present how to apply methods such as Analysis of Variance (ANOVA), K Means Cluster, Principal Components Analysis (PCA), Partial Least Squares (PLS), Multiple Factor Analysis (MFA), Multiple Correspondence Analysis (MCA) and Text Explorer to sensory and consumer data, emphasizing how each of these procedures operates, how each is interpreted, and how they relate to one another. By illustrating the best methods to address sensory and consumer preference problems, the goal of this paper is to familiarize analysts with sensory evaluation, consumers' preferences and appropriate multivariate methods so that each analyst can effectively use these methods in JMP for their own sensory studies.