This chapter presents an analysis on Zernike Moments from the class of orthogonal moments which are invariant to rotation, translation and scaling. The chapter initially focuses on the review of Zernike moments as 1D, 2D and 3D based on their dimension and later investigates on the construction, characteristics of Zernike Moments and their invariants, which can be used as a shape descriptor to capture global and detailed information from the data based on their order to provide outstanding performance in various applications of image processing. Further the chapter also presents an application of 2D Zernike Moments features for plant species recognition and classification using supervised learning techniques. The performance of the learned models was evaluated with True Positive Rate, True Rejection ratio, False Acceptance Rate, False Rejection Ratio and Receiver Operating Characteristics. The simulation results indicate that the Zernike moments with its invariants was successful in recognising and classifying the images with least FAR and significant TRR.