In the traditional cloud type recognition method, a set of features describing the color, texture and shape features of clouds are extracted, in which there are some irrelevance and redundancy features leading to the reduced recognition rate of cloud type. Based on the criteria of the max relevance and min redundancy (mRMR), symmetrical uncertainty is employed to overcome the inherent defect of mutual information, which tends to have more value attributes. The improved mRMR feature selection method is putted forward, and the best feature subsets are selected by this method, and then the support vector machine is used to the recognition of cloud type. Experimental results show that the correct recognition rate of altocumulus, cirrus, clear, cumulus, and stratus are improved significantly, with the total recognition rate being 86.96%; after feature selection, the total recognition rate can increase to 89.04%, and the recognition rate increases by 2%. For cloud type classification research, the texture feature is better than the shape feature; the shape features based on Zernike moment is better than HU moment invariants; the texture feature based on the gray level co occurrence matrix is the optimum feature extraction method.