Rice released varieties are genetic resources bulked with good genes. To define the potentials of these germplasm, genetic divergence analysis must be done. The study used different statistical tools such as descriptive statistics, Kolmogorov-Smirnov test, Shannon-Weaver diversity index (H’), correlation statistics (r), principal component analysis (PCA), Dixon’s test and clustering statistics in evaluating 29 NSIC (National Seed Industry Council) released varieties based on 11 morphological traits. Descriptive statistics showed significant differences on the traits used while following a normal distribution. Shannon-Weaver diversity derived a range of 0.55 (number of filled grain per panicle, NFGP) to 0.91 (grain yield, GY and number of tillers, NT) that infer moderate to high diversity traits. Correlation statistics among traits showed a range of r = -0.55 to 0.84 which GY was noted to positively correlate to all traits. PCA accounted 39.95% and 26.10% for PC1 and PC2, respectively. Notable component loading for the yield component traits such as panicle weight (PW) showed the highest contributor of positive projections in two PCs that explained 66.05% of the variation. PCA also detected two latent traits such GY and spikelet fertility (SF) as confirmed in Dixon’s test where outlier was found in SF and to yield contributing traits. Clustering statistics separated varieties into 5 clusters with a range of 5.88 to 106.22 euclidean distance (ED). Among the clusters, 5th cluster composed of one variety, NSIC Rc240 gave the highest GY (7.07 tha-1), NFGP (152.67), one thousand grain weight (24.77 g), PW (5.08 g) and spikelet number per panicle (185.33). The variety could potentially be adapted and a good source of genes for rice improvement localize at General Santos City.