When using the method of kernel principal component analysis(KPCA) to extract feature of target in radar target recognition, the HRRP characteristic is ignored. A translation invariant features-central moments was extracted as feature vector, KPCA was used to reduce the dimensionality; The BP neural network was easy to fall into local minimum, the genetic algorithm(GA) was used to optimize the BP network node weights and threshold. The experimental results based on the measured radar data show that the translation invariant KPCA feature extraction method achieve the combination of translation invariant and descending dimension, and the BP neural network optimized by GA improves the stability of classifier and improves the defect of falling into local minimum easily.