Principal Component Analysis（PCA） and Minor Component Analysis（MCA） are essential techniques in signal processing such as feature extraction

data compression

frequency estimation and curve fitting.Recently

there has been much interest in the connection between PCA

MCA and neural networks.It is a difficult problem in PCA and MCA when eigenvalues of covariance matrix R are not distinct.A new learning algorithm that the weight vectors will converge to orthonormal eigenvectors is proposed in this paper to solve the problem.