The purpose of this study was to capture some factors affecting wool fineness (WF) in Karacabey Merino. For this goal, CHAID (Chi-Square Automatic Interaction Detector) tree-based algorithm implemented to construct a regression tree diagram was specified based on Bonferroni adjustment within the scope of the prediction of wool fineness as a response variable. Also, Multivariate Adaptive Regression Splines (MARS) was implemented for the WF prediction. In the prediction of wool fineness (WF), sex, dam age (DA), birth weight (BW), birth type (BT), live body weight (LBW), greasy fleece weight (GFW), staple length (SL), number of fibers (F) and average number of crimps over a length of 5 cm (ANC) were considered as possible predictors. To guarantee the highest predictive accuracy of the CHAID algorithm, minimum animal numbers in parent and child nodes were thought as 4 and 2. Model fit statistics showed the powerful predictive performance of the CHAID and MARS algorithms, but MARS outperformed CHAID. Considering the regression tree diagram generated by CHAID algorithm, the most influential predictor affecting WF was F, followed by BW, ANC and DA at the 2nd significance degree, and SL at the 3rd significance degree, respectively. MARS predictive model with the selected 5 terms captured only F as a significant predictor. In conclusion, CHAID and MARS data mining algorithms reflected that F predictor may be considered as an indirect selection criterion in the characterization of the breed standards of the Karacabey Merino in wool characteristics for breeding goals.