Abstract: To search for a method to predict accurately carcass traits in bovine, in this study, DPS and SAS software were used to compare the methods of partial least squares regression, GM(1,N) gray system and BP neural network, in order to observe their accuracy in predicting carcass traits in bovine. Seven preslaughter growth traits including body height, body length, chest circumference, abdominal circumference, cannon bone circumference, live weight and average daily gain were used to predict the carcass weight and meat weight. The results showed that the partial least squares regression gave the highest accuracy, while the average relative errors of GM(1,N) gray system and BP neural network were lower. In this study, the three predicted results were combined and their mean value were calculated as the predictive values, which would greatly improve the accuracy of prediction. The results would provide some scientific references to beef production.