Data containing many variables (measured characters) are often collected in agricultural science research. An interpretative analysis of multivariate data considers all the variables simultaneously is required, and entails the use of multivariate statistical analysis. This paper provides a non-statistical, practical overview of the commonly used multivariate methods in agricultural science research, including principal component analysis, correspondence analysis, factor analysis, cluster analysis, discriminant analysis, path analysis, and canonical correlation analysis. The meaning, applicability, analytical procedures, and results interpretation of these methods are presented with cited examples. Our objective is to provide the researcher an intuitive understanding of multivariate analysis and their applications in agriculture.