Recognition of pain in equines (such as horses and donkeys) is essential for their welfare. However, this assessment depends solely on the ability of the observer to locate visible signs of pain since there is no verbal communication. The use of Grimace scales is proven to be efficient in detecting pain but is time-consuming and also dependent on the level of training of the annotators and, therefore, validity is not easily ensured. There is a need for automation of this process to help training. This work provides a system for pain prediction in horses, based on Grimace scales. The pipeline automatically finds landmarks on horse faces before classification. Our experiments show that using different classifiers for different poses of the horse is necessary, and fusion of different features improves results. We furthermore investigate the transfer of horse-based models for donkeys and illustrate the loss of accuracy in automatic landmark detection and subsequent pain prediction.