Convolutional neural network (CNN) has demonstrated its accuracy and speed in image registration of structural MRI. We designed an affine registration network (ARN), based on CNN, to explore its feasibility on image registration of perfusion fMRI. The six affine parameters were learned from the ARN using both simulated and real perfusion fMRI data and the transformed images were generated by applying the transformation matrix derived from the affine parameters. The results demonstrated that our ARN markedly outperforms the iteration-based SPM algorithm both in simulated and real data. The current ARN is being extended for deformable fMRI image registration.