Abstract: This paper proposes a new method for texture of classification by incorporating the design of undecimated wavelet transform based feature extractor with the design of an Euclidean distance based classifier.Variance,skewness,kurtosis,the combination of the above three statistical moments and spectral histograms at the outputs of undecimated wavelet decomposition are used to characterize each nonoverlapping window of the texture image.A feature extractor using linear transformation matrix is further employed to extract the classification-oriented features.With an Euclidean distance based classifier,each nonoverlapping window of the texture image is then assigned to its corresponding category.Minimization of the classification error is achieved by incorporating the design of the feature extractor with the design of the classifier based on Minimum Classification Error（MCE）training method.The proposed method has been evaluated on the classification of 25 BrodTex texture,and more than 90% classification accuracy has been achieved.