Statistical methods are widely used for solving of the problems of automatic control of the industrial objects, because they enable us to determine their dynamic characteristics during normal operation of the objects. The statistical correlation method for determination of these dynamic characteristics is based on the solution of an integral equation, which includes the correlation functions RXX(t) and RXY(t) of the input Х(t) and of the output Y(t) signals. It allows us to obtain the dynamic characteristics of an object without a disruption of its regular operation mode. However, application of these methods for construction of the mathematical models of the real-life industrial objects presents the following problem. Interferences and noises are imposed upon the useful signal, hindering the calculation of the estimates of their static characteristics. This paper presents one possible option for creation of the alternative methods and technologies for elimination of the error induced by noise during formation of the correlation matrices. The proposed general algorithms allow a reduction of these matrices to the similar matrices of the useful signals. The two presented alternative robust technologies enable one to solve these problems both in the absence of a correlation between the useful signal and the noise, and in the presence of such. The validity of the result is controlled by comparison of the obtained estimates of the elements of matrices by both methods. In many real-life industrial objects we encounter a need to apply the procedure for normalization of their elements. This leads to an additional error, which also leads to a disruption of the adequacy of the results. The authors propose general methods and technologies for elimination of that error.