This paper presents an adaptive anchor-point selection method for single image super-resolution (SR), which is based upon internal example-based SR model via locality constrained anchored neighborhood regression. The anchor points are fixed in anchored SR methods and are not flexible and customized for different input low-resolution (LR) images. To overcome this defect, we adaptively select anchor points via constructing customized training set for different input LR images, which can be realized by an internal example-based SR method. We introduce a locality-constrained anchored neighborhood regression to learn the relationship between LR space and high-resolution (HR) space. Extensive experimental results demonstrate that the performance of proposed method is competitive with several state-of-the-art SR methods.