In this work, an information theoretic input variable subset selection (ITSS) scheme for neural network based modeling of chemical processes is proposed. In recent years, artificial neural network models have been shown to be useful empirical models for modeling complex nonlinear chemical processes. ITSS selects an informative subset to be used as input data for constructing a neural network model. ITSS can select appropriate subsets for neural network model development, regardless of the dependencies between the process outputs and inputs. The power of the ITSS method is illustrated through its application to three example problems. Results obtained show that ITSS is capable of identifying subsets for developing viable artificial neural network models. As it uses a smaller set of input variables, ITSS can help identify simpler neural models with better generalization and ease of interpretability.