Because of the existing interactions among the variables of a multiple input-multiple output (MIMO) nonlinear system, its identification is a difficult task, particularly in the presence of uncertainties. Cement rotary kiln (CRK) is a MIMO nonlinear system in the cement factory with a complicated mechanism and uncertain disturbances. The identification of CRK is very important for different purposes such as prediction, fault detection, and control. In the previous works, CRK was identified after decomposing it into several multiple input-single output (MISO) systems. In this paper, for the first time, the rough-neural network (R-NN) is utilized for the identification of CRK without the usage of MISO structures. R-NN is a neural structure designed on the base of rough set theory for dealing with the uncertainty and vagueness. In addition, a stochastic gradient descent learning algorithm is proposed for training the R-NNs. The simulation results show the effectiveness of proposed methodology.