Principal components analysis is one of the multivariate statistical methods and was used to access the genetic diversity of 32 sunflower hybrids developed at the Department of Plant Breeding and Genetics, University of Agriculture, Faisalabad during 2014-15. Eight males were crossed with 32 females to produce 32 F1 hybrids using North Carolina Mating Design-I. Data were recorded on plant height, head diameter, percent filled achenes, 100-achene weight, achene yield per plant, harvest index, oil content, palmitic acid, stearic acid, oleic acid and linoleic acid. The first four principle components with Eigen value greater than one contributed 69.28 % of the total variability. The first principal component showed higher values for 100-achene weight (0.860), achene yield per plant (0.903), harvest index (0.777), second had higher values for oleic acid (-0.780) and linoleic acid (0.834), third had higher values for head diameter (-0.749) and percent filled achenes (0.782), whereas the fourth was associated with plant height (0.741). Four distinct groups can be differentiated on the basis of biplot diagram. H-1 and H-32 were the best hybrids having higher achene (94.63g, 91.45g)  and oil yield (46.24%, 44.08%) respectively.