Abstract: A new method of bubble recognition using optical underwater imaging was presented by employing Zernike moments and gray gradient, to differentiate bubbles from solid particles. This method included 3 parts: image division, image pre-processing and feature extraction for bubble recognition. Firstly, images of the suspended particles were obtained from underwater particle database, in which a particular bubble was divided and selected manually from the whole. Secondly, image pre-processing was employed to enhance single bubble images, to extract and represent bubble silhouette and gray level. Thus, the database of bubble features were selected and formed. Finally, the shape descriptor, Zernike moments, was utilized to measure the similarity with features of other suspended particles to differentiate circle particles from the irregular ones. Subsequently, the center of circle particle and the trend of gray gradient were computed, so as to distinguish the bubbles from solid particles. The experimental results show that, the accuracy of bubble recognition is up to 94%. It is concluded that this method not only recognizes bubbles from irregular suspensions, but also improves gray gradients calculation for enhanced results. By extracting and distinguishing object features through the prospects of both shape and gray, this method enhances the accuracy of bubble recognition, with higher precision and broader suitability.