In nature, the physical development of the body that takes place in parallel to the cognitive development of the individual has been shown to facilitate learning. This opens up the question of whether the same principles could be applied to robots in order to accelerate the learning of controllers and, if so, how to apply them effectively. In this line, several authors have run experiments, usually quite complex and heterogeneous, with different levels of success. In some cases, morphological development seemed to provide an advantage and in others it was clearly irrelevant or even detrimental. Basically, morphological development seems to provide an advantage only under some specific conditions, which cannot be identified before running an experiment. This is due the fact that there is still no agreement on the underlying mechanisms that lead to success or on how to design morphological development processes for specific problems. In this paper, we address this issue through the execution of different experiments over a simple, replicable, and straightforward experimental setup that makes use of different neural network controlled walkers together with a morphological development strategy based on growth. The morphological development processes in these experiments are analyzed both in terms of the results obtained by the different walkers and in terms of how their fitness landscapes change as the morphologies develop. By comparing experiments where morphological development improves learning and where it does not, a series of initial insights have been extracted on how to design morphological development processes.