Convolutional neural network (CNN) has been famous for its translation-invariant ability in feature learning. In order to further encounter rotation-invariant, data augmentation by rotation of training samples should be considered for multiple-branch based structure using maximum operator or average operator. In this paper, a novel Polar Coordinate CNN (PC-CNN) is proposed for rotation-invariant feature learning. Specifically, training samples are first input to a polar coordinate transform layer by which rotation-invariance is converted into translation-invariance. Consequently, rotation-invariance problem in feature learning can be easily encountered by traditional CNNs without the multiple-branch structure. Experimental results over two benchmark data sets demonstrate that the proposed polar transformation is very effective to encounter rotation-invariant into traditional CNNs and outperforms several state-of-the-art rotation-invariant CNNs.