We describe a new approach to multiple class pattern classification problems with noise and high dimensional feature space. The approach uses a random matrix X which has a specified distribution with mean M and covariance matrix r i j ( Σ s + Σ ε ) between any two columns of X . When Σ ε is known, the maximum likelihood estimators of the expectation M , correlation Γ , and covariance Σ s can be obtained. The patterns with high dimensional features and noise are then classified by a modified discriminant function according to the maximum likelihood estimation results. This new method is compared with a multilayer feed forward neural network approach on nine digit recognition tasks of increasing difficulty. Both methods achieved good results for those classification tasks, but the new approach was more effective and more efficient than the neural network method for difficult problems.