Previous research on facial expression recognition mainly focuses on near frontal face images, while in realistic interactive scenarios, the interested subjects may appear in arbitrary non-frontal poses. In this paper, we propose a framework to recognize six prototypical facial expressions, namely, anger, disgust, fear, joy, sadness and surprise, in an arbitrary head pose. We build a multi-pose training set by rendering 3D face scans from the BU-4DFE dynamic facial expression database  at 49 different viewpoints. We extract Local Binary Pattern (LBP) descriptors and further utilize multiple instance learning to mitigate the influence of inaccurate alignment in this challenging task. Experimental results demonstrate the power and validate the effectiveness of the proposed multi-pose facial expression recognition framework.