<p>Most of the problems I have seen were solved with 1-2 hidden layers. It is proven that MLPs with only one hidden layer are universal function approximators (<a href="http://deeplearning.cs.cmu.edu/pdfs/Kornick_et_al.pdf" rel="nofollow noreferrer">Hornik et. al.</a>). More hidden layers can make the problem easier or harder. You usually have to try different topologies. I heard that you cannot add an arbitrary number of hidden layers if you want to train your MLP with backprop because the gradient will become too small in the first layers (I have no reference for that). But there are some applications where people used up to <a href="http://arxiv.org/abs/1003.0358" rel="nofollow noreferrer">nine layers</a>. Maybe you are interested in a <a href="http://yann.lecun.com/exdb/mnist/" rel="nofollow noreferrer">standard benchmark problem</a> which is solved by different classifiers and MLP topologies.</p>
How to choose number of hidden layers and nodes in neural network?
<p>What does number of hidden layers in a multilayer perceptron neural network do to the way neural network behaves? Same question for number of nodes in hidden layers?</p>

<p>Let's say I want to use a neural network for hand written character recognition. In this case I put pixel colour intensity values as input nodes, and character classes as output nodes. </p>

<p>How would I choose number of hidden layers and nodes to solve such problem?</p>