The aim of this study was to find the best one among CHAID (Chi-square Automatic Interaction Detector), Exhaustive CHAID, and CART (Classification and Regression Tree) data mining algorithms in the prediction of body weight (BW) from several body measurements (abdominal width (AW), body length (BL), chest circumference (CC), chest depth (CD), face length (FL), front shank circumference (FSC), head circumference (HC), head length (HL), head width (HW), leg length (LL), tail length (TL), rear chest width (RCW), rump elevation (RE), rump width (RW), withers height (WH)) measured easily from three Kangal (Karabash) dog color varieties (Dun/Fawn, Grizzle, and Ashy) maintained in Sivas and Konya provinces, Turkey. Several goodness-of-fit criteria (coefficient of determination (R2%), adjusted coefficient of determination (Adj.R2%), coefficient of variation (CV%), SD ratio, Root Mean Square Error (RMSE), Relative Approximation Error (RAE), Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE), and Pearson correlation between actual and predicted values were estimated for describing the most suitable algorithm in terms of the predictive performance.  r values are 0.846, 0.838 and 0.732 for CHAID, Exhaustive CHAID and CART algorithms, respectively. RMSE values are 4.966, 5.083 and 6.349 for CHAID, Exhaustive CHAID and CART algorithms, respectively. The most important predictors are BE of BW for all algorithms. Among the algorithms, CHAID provided the most appropriate predictive capability in the prediction of the BW characteristic. The heaviest average BW of 61.375 kg was obtained from the subgroup of those having FSC > 14 cm and RE > 80 cm. The secondly heaviest average BW (53.455kg) was found for the subgroup of those having FSC > 13 cm and 74.000   RE  80 cm in Sivas province of Turkey. Consequently, it is hoped that the results of the study on the morphological characterization of Kangal dog varieties might be a good reference for next dog breeding studies.