A single layer network is a simple structure consisting of neurons each having inputs. The system performs a mapping from the -dimensional input space to the -dimensional output space. To train the network the same learning algorithms as for a single neuron can be used.
This type of network is widely used for linear separable problems, but like a neuron, single layer network are not capable of classifying non linear separable data sets. One way to tackle this problem is to use a multilayer network architecture.