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Generally, training images are essential for a computer vision model to classify specific object class accurately. Unfortunately, there exist countless number of different object classes in real world, and it is almost impossible for a computer vision model to obtain a complete training images for each of the different object class. To overcome this problem, zero-shot learning algorithm was emerged to learn unknown object classes from a set of known object classes information. Among these methods, attributes and image hierarchy are the widely used methods. In this paper, we combine both the strength of attributes and image hierarchy by proposing Attributes Relationship Model (ARM) to perform zero-shot learning. We tested the efficiency of the proposed algorithm on Animals with Attributes (AwA) dataset and manage to achieve state-of-the-art accuracy (50.61%) compare to other recent methods. [ABSTRACT FROM AUTHOR]