Echo state networks (ESNs) offer a simple learning algorithm for dynamical systems. It works by training linear readout neurons that combine the signals from a random, fixed, excitable "dynamical reservoir" network. Often the method works beautifully, sometimes it works poorly - and we do not really understand why. This contribution discusses phenomena related to poor learning performance and suggests research directions. The common theme is to understand the reservoir dynamics in terms of a dynamical representation of the task's input signals.