Like most machine learning algorithms, Echo State Networks possess several hyperparameters that have to be carefully tuned for achieving best performance. For minimizing the error on a specific task, we present a gradient based optimization algorithm, for the input scaling, the spectral radius, the leaking rate, and the regularization parameter.