Researchers begin to realize that near infrared spectroscopy analysis model can be simplified by removing some redundant variables from the full-spectrum with the growing understanding of near infrared spectroscopy. It is obvious that the simplified model constructed with retained informative variables can be interpreted more easily. Moreover, both prediction performance and robustness of calibration model can be improved wi hvariable selection, which has been proved in numerous applied examples. Therefore, variable selection has become a critical step in the process of constructing near infrared spectroscopy analysis models, and various kinds of variable selection algorithms and their derivative algorithms have been developed by chemometrics scientists. In order to help the researchers in near infrared spectroscopy analysis field to have a fast overview on variable selection algorithms, we try to review some variable selection algorithms commonly used in near infrared spectroscopy area in this article, including their main rationales and characteristics. These variable selection algorithms are divided into five categories according to their different features. These algorithms are based on parameters of partial least squares (PLS) model, intelligent optimization algorithms, successive projections strategy, model population analysis strategy, and spectral intervals respectively. During the process of carding literatures, we find that the development trends of variable selection algorithms mainly focus on two points: firstly, complexity of new proposed algorithms increaces continually; secondly, the combination of different algorithms becomes more and more popular. Furthermore, we also summarized several specific applied problems that may be occurred when variable selection algorithms are applied in near infrared spectroscopy analysis area. For example, how do different spectral pretreatment methods affect the performance of variable selection algorithm? How to address the poor stability and reliability of some variable selection algorithms?