Soft sensors have been used in industrial plants to estimate process variables that are difficult to measure online. Soft sensor models predicting an objective variable should be constructed with only important explanatory variables in terms of predictive ability, better interpretation of models and lower measurement costs. Besides, some process variables can affect an objective variable with time-delays. Therefore, we have proposed the methods for selecting important process variables and optimal time-delays of each variable simultaneously, by modifying the genetic algorithm-based wavelength selection method that is one of the wavelength selection methods in spectrum analysis. The proposed methods can select time-regions of process variables as a unit by using process data that includes process variables that are delayed in the range from zero to a set/given maximum value. The case study with simulation data and real industrial data confirmed that predictive, easy-to-interpret, and appropriate models were constructed using the proposed methods. (C) 2012 American Institute of Chemical Engineers AIChE J, 58: 18291840, 2012