Technical Abstract: Chloroplasts are organelles found in cells of green plants and eukaryotic algae that conduct photosynthesis. Knowing a protein’s subchloroplast location provides in-depths insights about the protein’s function and the microenvironment where it interacts with other molecules. Despite the chloroplast proteome projects and several computational methods for identifying chloroplast proteins, there are only a very limited number of methods for predicting proteins’ subchloroplast locations. In this paper, we present a bit-score weighted K-nearest neighbor method for predicting protein subchloroplast locations. The method makes prediction based on the bit-score weighted Euclidean distance calculated from the composition of selected pseudo-amino acids. Our method achieves 76.4% overall accuracy in assigning proteins to 4 subchloroplast locations in cross-validation. When tested on an independent set that was not seen by the method during the training and feature selection, the method achieves a consistent overall accuracy of 76.0%. Comparisons showed that it outperformed previously published methods. The method was also applied to predict subchloroplast locations of proteins in the chloroplast proteome. The software and datasets of the proposed method is available at https://edisk.fandm.edu/jing.hu/bsknn/bsknn.html.