This paper describes a new texture feature estimation technique for discriminating images of eight different grades of CTC (cutting, tearing, and curling) tea. This new set of feature vectors can discriminate the images of different sized tea granules with more efficiency than the statistical feature vectors do. The technique conjugates the feature information of one group of images along with the information of rest of the groups. This is executed by considering range of different groups of images of the same granule size. Indeed, ranges are estimated using the existing statistical texture features, namely variance, entropy and energy, in difference form. Daubechies' wavelets transform (WT) based sub-band images are utilized for calculating these statistical features. The techniques, for estimating these ranges and calculating the final feature set, adopt a simplified version of Mahalanobis distance calculation. Later, the data visualization method, principal component analysis (PCA), which is used to visualize the existing classes of textures, has found distinguishable characteristics among the new feature sets. It is further observed that the unsupervised clustering algorithm self organizing map (SOM) can classify the images efficiently into appropriate clusters. Two neural networks, namely multi-layer perceptron (MLP) network and learning vector quantization (LVQ) were used for texture classifications. The classification accuracy, for example 74.67% and 80% in MLP and LVQ, respectively, outperforms the other results obtained by using existing statistical texture features. (c) 2006 Elsevier Ltd. All rights reserved.