Abstract: The extending fractal characteristics of aircraft echoes from low-resolution radars offer a description of echo roughness with different scales, therefore they can provide a new way for aircraft target classification and recognition with surveillance radars. Firstly, on basis of introducing extending fractal theory, the paper analyzes the extending fractal characteristics of echoes from aircraft targets in a VHF-band surveillance radar by means of the extending fractal analysis. Secondly, it puts forward an extending-fractal-feature-based classification method for aircraft targets with low-resolution surveillance radars from the viewpoint of pattern recognition. Finally, it does classification experiments with real recorded echo data from different types of aircraft targets, and analyzes the performance of the proposed method. The experimental results show that the extending fractal characteristic parameters such as the generalized Hurst exponents can be used as effective features for aircraft target classification and recognition, the proposed method has a nicer classification performance, and it is an effective classification method.