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Abstract: Selection of proper phenotypic trait among various traits related with interesting performance plays an important role in genetic evaluation. In this study, principal components analysis (PCA) was adapted to generate a new index as a measure of racing performance of 12,279 horses. This method allows us to reduce the number of variables considered in the evaluation of the horses'' racing performance, which may facilitate modeling genetic programs. The resulted racing time, earning prize and rank were selected for generating new indices as the representation of racing performance of the horses. Three indices used in this study were: 1) PCA1 generated from the modified values of racing time, earning prize and rank, 2) PCA2 generated from the modified racing time and rank, and 3) the adjusted racing time. The first principal components (PCs), elements in the eigenvector corresponding to the largest eigenvalue of PCA, of PCA1 and PCA2 explained the variance of the selected variables about 75.6% and 75.4% respectively. Linear combinations of the first PCs and adjusted variables were used as new performance indices. Those animal models were composed of significant explanatory variables selected by Akaike information criterion (AIC). Heritability and repeatability were 0.324 (±0.026) and 0.334 (±0.034) for adjusted racing time, 0.319 (±0.014) and 0.326 (±0.018) for PCA1, and 0.324 (±0.010) and 0.332 (±0.012) for PCA2 respectively. Estimated heritabilities and repeatabilities for three indices showed similar values for domestic racing records. However, models using PCA showed better fitting for data than model using racing time as a performance index. The proposed methodology is efficient to evaluate the total variance in this group of correlated traits, allowing reduction in the number of variables for genetic evaluation and construction of better fitting model. [Copyright &y& Elsevier]