As a nonlinear dimension reduction algorithm, Gaussian process latent variable model (GPLVM) has been widely applied in pattern recognition and computer vision for its capability in dealing with small size and high-dimensional samples. As GPLVM can discover low-dimensional manifolds in high-dimensional data given only a small number of samples, a new SAR target recognition method was proposed, in which a modified GPLVM was used for feature extraction and Gaussian process classification was employed as the classifier. In GPLVM, the likelihood was optimized by using the scaled conjugate gradient. In order to avoid the noise effect to gradient estimate and overcome the disadvantage that the performance is severely affected by the step length, the immune clone selection algorithm based GPLVM was developed for target feature extraction where the immune clonal selection algorithm characterized by rapid convergence to global optimum was utilized to improve the performance. The experimental results show that the method not only reduces the dimension but also gets higher accuracy.