In this study, the effects of splicing parameters, fiber and yarn properties on the tenacity and elongation of spliced yarns were investigated in detail. For this purpose, yarns from eight different cotton types, having three different counts (29.5, 19.7 and 14.8 tex) and three different twist coefficients (alpha(tex) 3653, alpha(tex) 4038, alpha(tex) 4423) were produced. Fiber properties measured using an Advanced Fiber Information System fiber tester were evaluated. Artificial neural network and response surface models were used to analyze spliced yarn tenacity and elongation as dependent variables. As independent variables, fiber properties together with the machine settings such as opening air, splicing air and splicing time, yarn twist and yarn count were chosen. As a result of the study, equations and neural network models that predict the tenacity and elongation of the spliced yarns were obtained. The obtained equations and models are statistically important and have high coefficient of multiple determination (R(2)).