Agronomic research often involves measurement and collection of multiple response variables in an effort to understand the more complex nature of the system being studied. Multivariate statistical methods encompass the simultaneous analysis of all random variables measured on each experimental or sampling unit. Many agronomic research systems studied are, by their very nature, multivariate; however, most analyses reported are univariate (analysis of one response at a time).  The objective of this workshop is to use a hands-on approach to familiarize the researcher with a set of common applications of multivariate methods and techniques for the agronomic sciences: principal components analysis, multiple regression, and discriminant analysis. Agronomically relevant data sets and examples will be used throughout the workshop. We will provide the attendees with a taxonomical key for multivariate techniques, a list of relevant references for each technique, as well as R and SAS code to follow as we explore the data to assess quality and suitability for multivariate analyses. The workshop will focus on how multivariate methods can capture the concept of variability to better understand complex systems. Important considerations along with advantages and disadvantages of each multivariate tool and their corresponding research questions will be examined.