Stiffness is one of the most important utility properties of textile materials and plays a significant role in well-being due to its influence on physiological comfort [1]. On that point are a great deal of structural properties of textile materials also operating parameters (knitting+finishing) influencing stiffness and there are also statistically significant interactions between the principal factors determining the stiffness of textile materials. As part of our research, we proposed to facilitate the industry adjust the most relevant operating parameters before actual manufacturing to reach the desired stiffness and satisfy consumers. It warrants the application of artificial neural nets (ANNs) to predict the stiffness of finished knitted fabrics and the utilization of the Fuzzy Decision Tree in the selection procedure, to puzzle out the problem of insufficient data and boil down the complexity of predictive models. Moreover, a virtual leave one out approach dealing with overfitting phenomenon and allowing the selection of the optimal neural network architecture was applied.