Common bean (Phaseolus vulgaris L.) is regarded as grain of hope because of its role in subsistence agriculture, and source of food for millions of people in Africa alone. The purpose of this study was to estimate the extent of genetic variability of common bean agronomic traits. A total of 12 quantitative traits of forty-nine genotypes of common bean was studied. The experiment was conducted at Finoteselam, West Gojam, with triple lattice design. Results showed that genotypes had high values of genotypic and phenotypic variation for plant height, pod per plant, hundred seed weight, biological yield and grain yield. The mean square values of genotypes showed significant variation among all 49 genotypes. High heritability estimates were obtained for most of the traits ranged from 60-85.8%. The magnitudes of genotypic correlation were higher as compared to phenotypic correlations and in the same direction. Above ground dry biomass, seeds per plant, hundred seed weight and harvest index traits of common bean exerted positive direct influence on seed yield in both phenotypic and genotypic path coefficient analysis. Principal component analysis resulted in four principal components (PCs) with about 79.77% of the total variation of the tested genotypes with respect to the 12 traits. Cluster analysis based on general Euclidean distance of the 12 traits grouped the 49 bean genotypes into five clusters. In conclusion, the result of this study demonstrated that there is appreciable genetic variability among the common bean genotypes and a number of traits were found to have high broad sense heritability, high genetic advance, which could be used to improve the yield and other agronomic traits of the crop through selection. Due to the effect of the environment, it is recommended that the experiment have to be repeated over the years and in more different agro-climatic zones in order to get confirmation and full information about the observed genetic variability. Keywords: Cluster analysis, Correlation, Genetic advance, Path coefficient analysis, Principal component analysis