Industrial objects with delay are normally influenced by various kind of disturbance, especially the disturbance can not be mesured. The impacts of disturbance lower the quality of system control significantly, even in some cases, they make system unstable. The identification of unmesuared disturbance is a challenging problem, attracting the interests of many scientists. Therefore this paper proposes a disturbance identification method for the class of industrial objects inluding delay in order to determine disturbance and set a foundation to offset its impacts based on the using of Radial Basic Functions (RBF) Neural Networks and parallel model. The weights updated rule of RBF Neural Network ensures the convergence of disturbance indentification process and the system stabilization.