Abstract: In a high resolution radar system, range profiles contain rather detailed structure information of a target and thus provide a more reliable tool for target recognition. However, the complexity of radar targets and their environments lead to nonlinear relationships between different targets. For this reason, it is necessary to adopt some nonlinear methods to obtain satisfactory recognition results. Currently, kernel-based method is a focus in the field of pattern recognition and shows many advantages as to solve nonlinear problems. In this paper, several kernel-based nonlinear methods are studied and applied to radar range profile recognition. As it is known to all, kernel Fisher discriminant analysis (KFDA) is one of the most effective techniques for nonlinear feature extraction. But it always suffers from the small sample size (SSS) problem. In order to deal with this problem, a method, called null-KFDA, is given and used to extract features from a range profile. Then, a novel kernel-based nonlinear classifier, called KNR (kernel-based nonlinear representor), is introduced and applied for classification. Experimental results on measured profiles from three aircrafts indicate that the null-KFDA is able to extract optimal discriminant vectors and does not suffer from the SSS problem, and that the performance of KNR is superior to those obtained by nonlinear support vector machine (SVM) and radial basis function neural network (RBFNN) in terms of both recognition rate and efficiency.