Multilayer perceptrons are networks of perceptrons. Ian Witten explains how they can implement arbitrary decision boundaries using "hidden layers".
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Multilayer perceptrons are networks of perceptrons, networks of linear classifiers. In fact, they can implement arbitrary decision boundaries using “hidden layers”. Weka has a graphical interface that lets you create your own network structure with as many perceptrons and connections as you like. A quick test showed that a multilayer perceptron with one hidden layer gave better results than other methods on two out of six data sets – not too bad. But it was 10–2000 times slower than other methods, which is a bit of a disadvantage.