Tongue fissures, an important feature on the tongue surface, may be pathologically related to some diseases. Most existing tongue fissure extraction methods use tongue images captured by traditional charge coupled device cameras. However, these conventional methods cannot be used for an accurate analysis of the tongue surface due to limited information from the images. To solve this, a hyperspectral tongue imager is used to capture tongue images instead of a digital camera. New algorithms for automatic tongue fissure extraction and classification, based on hyperspectral images, are presented. Both spectral and spatial information of the tongue surface is used to segment the tongue body and extract tongue fissures. Then a classification algorithm based on a hidden Markov model is used to classify tongue fissures into 12 typical categories. Results of the experiment show that the new method has good performance in terms of the classification rates of correctness.