One-dimension range profile has recently been widely used as recognition feature in radar target recognition since it can be extracted easily. However, the one-dimension range profile of a target has strong dependence on the aspect-angle of the target because it is based on the model of scattering centers. On the other hand, the different classes of target samples overlap each other in the feature space, so satisfactory recognition results cannot be obtained directly for the targets under whole posture. In order to improve the recognition performance of systems based on one-dimension range profile, a method of radar target recognition based on the multi-resolution analysis theory and neural network is presented. Firstly, one-dimension range profile features under various resolutions are obtained by using multi-resolution analysis. Then, the tree-structured cascade SOM network – a hybrid neural network on the basis of the multi-resolution analysis theory and self-organizing map (SOM) – has been used as the classifier to make multi-grade judgment and recognition gradually for the one-dimension range profile features. The experimental results show that the average correct recognition rate for three kinds of targets – a propeller aircraft, a little jet aircraft and a large jet aircraft – with 0–180° posture variation in aspect-angle is up to 92.97%.