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For multivariate systems, empirical modelling using statistical methods is an issue in the presence of multicollinearity. Recently, neural networks provide a wide class of general-purpose, flexible non-linear non-parametric mapping, ignoring the fact of underlying data property. In this work, a composite system is designed to integrate the statistical principles of multivariate data, particularly multicollinearity, into the neural network architecture. The designed system is trained and validated with a simulated database based on the extent of correlation structure among the input variables. A real-life case with a large multi-dimensional system is illustrated under the proposed system.