Regression models are normally characterized by only one output; however, taken into consideration the problem here presented, it make sense to think in a strategy based in MIMO regressors. In order to predict more than one output, it can be simple considered a regressor for each output, by concatenating several MISO regressors ; however, ignoring possible relationship among the constructed models could result in a drawback for the solution. A smarter solution is also suggested to construct several regressors not only by the input training data, but also by the possible internal relationships between them. In particular, this solution takes the advantage of constructing a MIMO regressor which is smaller than the size of those MISO regressors. It should be noted that the discussed solution leads to better predictions when there is a strong correlation between the features and the targets.