his article reviews the recent literature on the use of electrooculography (EOG) signals in diagnosing several ocular disorders. Since ocular disorders are becoming very common these days, it is essential to understand the role of EOG as a noninvasive technique to diagnose such conditions. This article introduces the principles of EOG signal acquisition, its typical characteristics, and the various paradigms of signal recording. The signal conditioning of EOG is discussed with respect to the latest techniques for removing noise and baseline drift. The use of the recent deep learning architectures for classifying EOG signals is discussed. The three major ocular conditions diagnosed with EOG are retinal, oculomotor, and vestibular dysfunctions. This article explores the manifestations of each condition in the EOG signal. This article further discusses subcategories of these conditions, such as the diagnosis of strabismus, nerve palsies, and vertigo-related disorders. A comparative analysis of EOG with other diagnostic methods, such as optical coherence tomography (OCT) and electroretinography (ERG), is provided. The review finally concludes by indicating the existing challenges.