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Considering the limitations of wavelet in image representation-that it is only optimal in representing point singularities and difficult to extract curve features of coal and rock images, a new recognition method for low-resolution coal-rock images based on curvelet transform was proposed.The method used curvelet transform to decompose images into curvelet coefficients in different scales.Then, PCA was applied to obtain a lower dimensional representation that was put into a k-NN classifier. Finally, the final recognition result was obtained via weighted fusion of classification results.Experimental results showed that the features extracted by curvelet decomposition could effectively express the curve features of coal-rock images.Compared with several other existing methods, the proposed method had higher recognition accuracy rate, with the average recognition rate reaching 95.0%.Under the condition of low image resolution can it also get high recognition rate and meet the real-time requirements of coal-rock recognition.