Soft sensors based on multiway partial least squares (MW-PLS) are often used to estimate, in useful time, the end quality of batch processes, due to their ability to deal with high dimensional and noisy data. However, PLS and its variants only bring parsimony to the variables' mode. The time mode, which is the main source of complexity in MW-PLS, remains unchanged. Parsimony on the time dimension can be achieved by manipulating the variables' resolution or granularity. In this article, we address the optimal selection of resolution for each individual batch variable, as an additional degree of freedom for maximizing the predictive performance of industrial soft sensors. The proposed methodology will conduct, simultaneously, the optimal selection of (1) variables, (2) resolutions, and (3) stages. At the end, a multiresolution PLS model (MR-PLS) will be obtained, that optimally predicts the batch-end quality within the class of all MW-PLS approaches. (C) 2018 American Institute of Chemical Engineers