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Principal Component Analysis (PCA) is a well-established multivariate analysis technique that helps in reducing dimensions of big datasets (possibly correlated variables) into a fewer number of uncorrelated variables known as principal components. Now a day's its application can be seen in biostatistics, marketing, sociology and other fields. In order to determine the pattern of variation with a view to identifying potential improvement in the parent population, this study aims to empirically demonstrate the application of the principal component analysis by the use of grain yield and its component characteristics data for fifty maize genotypes in the North-West plain area of India were collected from AICRP Annual Maize Progress Report of Kharif 2018. [ABSTRACT FROM AUTHOR]