It is known that biostatistics has a great role in many fields such as veterinary medicine and animal sciences. Animals and birds are major sources for human feeding (protein source) then, statistical analysis of animal characteristics is of great importance. The objective of this paper was to explain and apply an important statistical method called principle component analysis to extract new carcass trait components of Japanese quail from old variables. The idea of this method is that it forms a new variable (linear combinations of them) by reduction the dimension of the data for a large number of old variables. A total of 720 values of data were used to represent the variables under study for three different lines of Japanese quail. These variables were (live, slaughter, dressing, carcass, heart, liver, gizzard, and spleen) weight. SPSS packages used for calculation descriptive statistics, correlations and principal component reduction method. The results showed that Bartletts test of sphericity is highly significant (P = 0.000 **) for the three lines. Three principle components were able to explain 82.193% (53.927, 15.188, 13.078 for PC1, PC2, PC3 respectively) of the total variance in the eight variables of the high body weight line, two principle components were able to explain 76.429 % (62.504% and 13.925% for PC1 and PC2, respectively) of the total variance in the eight variables of the low body weight line and three principle components were able to explain 78.669% (42.363%, 22.478% and 13.827% for PC1, PC2, PC3 respectively) of the total variance in the eight variables in the random bred control line. Principal component analysis is an efficient method in determining carcass traits features and decreasing the messy in such type of biological data. This technique and its related techniques play an important role in many statistical methods like principal component regression.