Principal component analysis is a multivariate statistical method of data analysis which is used to reduce the dimension of the data. The reduction of dimension is achieved by forming new variables which are linear combinations of variables in the data set. These linear combinations are chosen to account for as much of the original variance-covariance/correlation structure in the original variables as possible. In PCA, our main aim to maximize the variance of a linear combination of the variables. This paper analyses Indian agricultural crop data which consists of the eight major crops reported to the country for the period 2016. The crops consist of rice, bajra, jwar, maize, marua, wheat, barley, and oth_care. In this paper, we applied the principal component analysis (PCA) to explain the correlation between the crops. PCA has recommended that only two PC’s explain 93% of the total variability of the data set. Data analysis was carried out using R- Software. The Scree and Loading plot shows that correlation exists between crops. The datasets for this study has been taken from Agriculture department, Govt. of India for the year 2016.