Most of the feature extraction methods is complex and is easily influenced by some uncertain factors. Based on the idea of multi-resolution decomposing theory, a new statistical feature extraction algorithm based on gray multi-channel decomposition is proposed in the paper. The image's gray is decomposed into some channels. The gray of an image is partitioned into some gray regions, which is also called gray channels. The gray features on each channel are analyzed and extracted. Then all the gray features form feature vector. The multi-channel feature extraction method based on multi-resolution decomposition using wavelet theory is introduced at first. Each channel's gray feature is extracted and formed the feature vector. Targets can be well distinguished. But the method not has invariant property when image rotates. Referenced the thought of multi-resolution decomposition, a new multi-channel feature extraction based on gray is proposed in the paper. The four features, which is average of gray, variance, number of pixel and peakedness, are extracted and form the feature vector. The vector is used to identify targets. With the new method, the shortage of the former method is overcome. And the method not only has invariant property of rotation, but also has invariant properties of proportion and translation. The robustness is perfect. And the calculation is simple. The features of some infrared images of tank and helicopter are extracted with the new method. The results show its effectivity. It's helpful for image feature extraction and target recognition.