Developments in multivariate statistical analysis have genesis in the parametrics surrounding the multivariate normal distribution in the continuous case while the product multinomial law dominates in discrete multivariate analysis. Characterisations of multi-normal distributions have provided a wealth of rigid mathematical tools leading to a very systematic evolution of mathematical theory laying down the foundation of multivariate statistical methods. Internal multivariate analyses comprising of principal component models, canonical correlation and factor analysis are all based on appropriate invariance structures that exploit the underlying linearity of the interrelation of different characteristics, without depending much on underlying normality, and these tools are very useful in many areas of applied research, such as sociology, psychology, economics, and agricultural sciences. In the recent past, there has been a phenomenal growth of multivariate analysis in medical studies, clinical trials and bioinformatics, among others. The role of multinormality is being scrutinized increasingly in these contexts.