The research on the classification and identification of microscopic residual oil occurrence states plays a vital role in residual oil exploitation and is of great significance for improving oil field recovery. In recent years, a large number of studies in this field have promoted the development of technologies for identifying microscopic residual oil by introducing deep learning. However, deep learning has not yet established a unified framework for microscopic residual oil identification, nor has it formed a standardized operation process. To guide future research, this study reviews existing methods for identifying residual oil and introduces the identification technologies for microscopic residual oil based on machine vision from several aspects, including image acquisition and classification standards, image processing, and residual oil identification methods. Residual oil identification methods are categorized into traditional and deep learning-based methods. The traditional methods are further divided into those based on manual feature extraction and those based on machine learning classification. The deep learning-based methods are divided into single-stage and two-stage methods. Detailed summaries are provided for data enhancement, pre-training, image segmentation, and image classification. Finally, this study discusses the challenges of applying deep learning to microscopic residual oil identification and explores future development trends.