A neural network is proposed based on a divide-and-conquer scheme. The network has gates which control firing of its hidden nodes. By opening and closing the gates depending on input values, the network divides the input space into sub-regions and assigns its nodes to each of them to produce the desired output in that region. The division mechanism is constructed by learning. A new learning method is proposed which divides the space in accordance to the difficulties; areas with larger errors are divided into smaller sub-regions. Thus, the nodes in the network are more densely assigned to areas with higher difficulties to ‘conquer’ the areas appropriately. Function approximation examples are provided to illustrate the validity of the proposed network.