In this paper, we study the problem of semantic part seg- mentation for animals. This is more challenging than stan- dard object detection, object segmentation and pose estima- tion tasks because semantic parts of animals often have sim- ilar appearance and highly varying shapes. To tackle these challenges, we build a mixture of compositional models to represent the object boundary and the boundaries of seman- tic parts.   And we incorporate edge,  appearance,  and se- mantic part cues into the compositional model. Given part- level  segmentation  annotation,  we  develop  a  novel  algo- rithm to learn a mixture of compositional models under var- ious poses and viewpoints for certain animal classes.  Fur- thermore, a linear complexity algorithm is offered for effi- cient inference of the compositional model using dynamic programming.  We evaluate our method for horse and cow using a newly annotated dataset on Pascal VOC 2010 which has pixelwise part labels. Experimental results demonstrate the effectiveness of our method.