Abstract: A probabilistic neural network (PNN) comprises a layer L1 of input nodes, a layer L2 of exemplar nodes, a layer L3 of primary Parzen nodes, a layer L4 of sum nodes, and optionally a layer L5 of output nodes. Each exemplar node determines the degree of match between a respective exemplar vector and an input vector and feeds a respective primary Parzen node. The exemplar and primary Parzen nodes are grouped into design classes, with a sum node for each class which combines the outputs of the primary Parzen nodes for that class and feeds a corresponding output node. The network includes for each primary Parzen node (e.g. L3-2-3P) for the design classes a secondary Parzen node (L3-2-3S), the secondary Parzen nodes all feeding a null class sum node (L4-0). Each secondary Parzen node has a Parzen function with a lower peak amplitude and a broader spread than the corresponding primary Parzen node, and is fed from the exemplar node for that primary Parzen node.