Lung field defines a region‐of‐interest in which specific radiologic signs such as septal lines, pulmonary opacities, cavities, consolidations, and lung nodules are searched by a chest radiographic computer‐aided diagnostic system. Thus, its precise segmentation is extremely important. To precisely segment it, numerous methods have been developed during the last four decades. However, no exclusive survey consolidating the advancements in these methods has been presented till date, thus indicating a void and the need. This study fills the void by presenting a comprehensive survey of these methods with a focus on their underlying principle, the dataset used, reported performance, and relative merits and demerits. It refrains from doing a hard comparative evaluation by bringing all of them on a common platform, since the datasets used in their development and testing are of varied quality, complexity, and are not publicly available. It also provides a glimpse of deep learning, the present state of deep‐learning‐based lung field segmentation methods, expectations from it, and the challenges ahead of it.