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The training problem for feedforward neural networks is nonlinear parameter estimation that can be solved by a variety of optimization techniques. Much of the literature on neural networks has focused on variants of gradient descent. The training of neural networks using such techniques is known to be a slow process with more sophisticated techniques not always performing significantly better. It is shown that feedforward neural networks can have ill-conditioned Hessians and that this ill-conditioning can be quite common. The analysis and experimental results lead to the conclusion that many network training problems are ill-conditioned and may not be solved more efficiently by higher order optimization methods. The analysis are for completely connected layered networks, they extend to networks with sparse connectivity as well. The results suggest that neural networks can have considerable redundancy in parameterizing the function space in a neighborhood of a local minimum, independently of whether or not the solution has a small residual.