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Wavelet-domain hidden Markov models (HMMs), in particular the hidden Markov tree (HMT), have been recentlyproposed and applied to image processing, e.g. denoising and segmentation. In this papel; texture analysis and classification using wavelet-domain HMMs are studied. In order to achieve more accurate texture characterization, we propose a new tree-structured HMM, called the 2 -0 HMT-3, where the wavelet coefficients from three subbands are grouped together: Besides the interscale dependencies, the proposed 2 -0 HMT-3 can also capture the dependencies across the wavelet subbands that are found useful for texture analysis. The experimental results show that the 2-D HMT-3 provides a nearly 20% improvement over the method using wavelet energy signatures, and the overall percentage of correct classification is over 95% upon a set of 55 Brodatz textures.