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Feature selection is useful on a variety of fronts: it is the best weapon against the Curse of Dimensionality; it can reduce overall training times; and it is a powerful defense against overfitting, increasing model generalizability. After some experiences, using stacked neural nets, parallel neural nets, asymmetric configs, simple neural nets, multiple layers, dropouts, activation functions etc there is one conclusion: There's NOTHING like a good Feature Selection. Accuracy and generalization power can be leveraged by a correct feature selection, based in correlation, skewness, t-test, ANOVA, entropy and information gain. In a time when ample processing power can tempt us to think that feature selection may not be as relevant as it once was, it's important to remember that this only accounts for one of the numerous benefits of informed feature selection -- decreased training times.