The paper proposes a new method for variable selection for prediction settings and soft sensors applications. The new variable selection method is based on the multi-layer perceptron (MLP) neural network model, where the network is trained a single time, maintaining low computational cost. The proposed method was successfully applied, and compared with four state-of-the-art methods in one artificial dataset and three real-world datasets, two publicly available datasets (Box–Jenkins gas furnace and gas mileage), and a dataset of a problem where the objective is to estimate the fluoride concentration in the effluent of a real urban water treatment plant (WTP). The proposed method presents similar or better approximation performance when compared to the other four methods. In the experiments, among all the five methods, the proposed method selects the lowest number of variables and variables-delays pairs to achieve the best solution. In soft sensors applications having a lower number of variables is a positive factor for decreasing implementation costs, or even making the soft sensor feasible at all.