Cement rotary kiln is the main part of cement production process that have always attracted many researchers’ attention. But this complex nonlinear system has not been modeled efficiently which can make an appropriate performance specially in noisy condition. In this paper Takagi-Sugeno neuro-fuzzy system (TSNFS) is used for identification of cement rotary kiln, and gradient descent (GD) algorithm is applied for tuning the parameters of antecedent and consequent parts of fuzzy rules. In addition, the optimal inputs of the system are selected by genetic algorithm (GA) to achieve less complexity in fuzzy system. The data related to Saveh White Cement (SWC) factory is used in simulations. The Results demonstrate that the proposed identifier has a better performance in comparison with neural and fuzzy models have presented earlier for the same data. Furthermore, in this paper TSNFS is evaluated in noisy condition which had not been worked out before in related researches. Simulations show that this model has a proper performance in different noisy condition.