The multi-target distortion invariance pattern recognition of three airplane models is studied by employing feedforward neural networks and backpropagation algorithm. With previous results for the same task by using cascaded neural networks and clustering encoding, the number of the hidden neurons and its effect on recognizing rate and weight training are studied. An efficient method for reducing the hidden neurons and simplifying the recognizing network structure is proposed.