A smoke concentration grading method based on the image analysis technique is proposed for the automatic speed adjustment of the dust removal fan in the copper smelting process. We obtain a sequence of sub images by using a moving window to slide over the whole smoke image from top to bottom. Then, discrete cosine transform (DCT) is utilized to extract the features of each sub-image and the DCT coefficients are vectorized as the observation data for hidden Markov model (HMM). Thus an image is divided into an observed sequence to build the HMM model for grade classification. Four different running states are considered in the smelting process, in which a HMM model is built for each running state. For each running state, 30 images are used for the training of HMM model. The results show that the classification accuracy can reach 95% with HMM, which is higher than that of least squares support vector machine (LSSVM).