In order to solve the problem of the subtle inter-class difference and large intra-class variation caused by different poses in fine grained object recognition, a new algorithm is proposed for fine-grained object recognition based on pose alignment and part based representation. In the training stage, firstly, dozens of typical poses of the object are summed up by clustering, and then a pose classifier is trained based on them which can be used to align object pose. Then, the fine-grained classifiers are trained by the visible parts and global feature ensembles in each pose subclass. Given an unseen test image, the probabilities that current test image belongs to each pose subclass are gotten via the pose classifier. The output from first k subclass classifiers is weighted combined to form a single classification decision. Our approach is especially valuable for fine-grained recognition when intra-class variations are extremely high. The proposed method is validated by the experimental results on the bird database CUB_2010-2011, which has the large intra-class variations. The results show that the proposed method has better performance than the existing methods.