A new method by integrating the multivariate statistical analysis with neural network used for complex pattern classification was proposed in this paper. First, a particularly developed statistical method called correlational components analysis was employed to extract pattern characteristics from the original sample pattern space. These pattern characteristics were then used as inputs to a multi-layered feedforward neural networks for further pattern classification, The proposed approach transforms the complex patterns into lower dimensional and mutually decoupled ones, it also takes the advantages of the self-learning capability of the neural networks. Finally, a practical example of natural spearmint oil was used to verify the effectiveness of the new method. The results showed that the proposed integrated approach gives better results than other conventional methods. A new method by integrating the multivariate statistical analysis with neural network used for complex pattern classification was proposed in this paper. First, a particularly developed statistical method called correlational components analysis was employed to extract pattern characteristics from the original sample pattern space. These pattern characteristics were then used as inputs to a multi-layered feedforward neural networks for further pattern classification, The proposed approach transforms the complex patterns into lower dimensional and mutually decoupled ones, it also takes the advantages of the self-learning capability of the neural networks. Finally , a practical example of natural spearmint oil was used to verify the effectiveness of the new method. The results showed that the proposed integrated approach gives better results than other conventional methods.