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A brief review of the latest automated image classification techniques employed for all-sky survey work is presented. Special emphasis is placed on the use of neural network pattern recognition techniques in the field of astronomy. In assessing the quality of image recognition derived from this method particular attention is given to the problem of star-galaxy discrimination in large digital sky surveys. A two color survey of the North Galactic Pole performed with the Minnesota Automated Plate Scanner is discussed. We assess the efficiency of image classification and sample completeness through comparisons with a variety of independent studies of the NGP area using a variety of detectors and image detection techniques. Finally, we describe current efforts by the APS group and others to perform quantitative galaxy morphological classification using advanced image processing algorithms and various pattern classification techniques.
