A 3D vector version of the back-propagation algorithm is proposed for multi-layered neural networks in which vector product operation is performed, and whose weights, threshold values, input and output signals are all 3D real numbered vectors. This new algorithm can be used to learn patterns consisted of 3D vectors in a natural way. The XOR problem was used to successfully test the new formulation.