In this paper, a scalable parallel image recognition method, CS-BoW (Class-Specific Bag of Words), is proposed. CSBoW builds submodels for each class using weighted BoW in training phase. In test phase, CS-BoW calculates and sorts the coding residual for every class of each test image, then each test image is assigned to the class which has the smallest coding residual. The proposed CS-BoW is easy to be scaled, the only thing to do to extend training dataset with a new class is to build a submodel for the new class. No extra calculation for existing data. CS-BoW calculates coding residual of each test image for every class, so it is convenient to obtain Top N accuracy, while many other image recognition methods can only release Top 1 accuracy. The experimental results show that CS-BoW achieves comparable accuracy in very short time if it is run parallelly, it can be as fast as 0.05s per image in caltech 101 and caltech 256.