Two-dimensional entropic thresholding methods apply gray-level spatial correlation to thresholding and achieve much better performance than 1-D methods, while suffering from large time consumption. To utilize gray-level spatial correlation in thresholding with less time consumption, we define and describe a new entropic thresholding approach employing the gray-level spatial correlation (GLSC) histogram. The GLSC histogram is determined using the gray value of the pixels and the number of their neighboring pixels of similar gray value, which is different from a 2-D histogram. During the entropic criterion function computation, the entropy yielded by different elements in the GLSC histogram is weighted by a nonlinear weighting function, which we suggest. In the experiment, Kapur's 1-D method and three 2-D methods reported by Abutaleb and Sahoo are employed for comparision. Experiments on many real-world images demonstrate that the proposed method yields equivalent or even better results than 2-D ones while saving time remarkably and significantly outperforms Kapur's 1-D method without too much more time consumption generally.