Classification is a data mining task and which is a two-phase process: learning and classification. The learning phase consists of constructing a classifier or a model from a labeled set of objects. The classification phase consists classifying new objects by using the generated classifier. Different approaches have been proposed for supervised classification problems through Formal Concept Analysis, and which is a mathematical theory to build upon hierarchies of formal concepts. The proposed approaches in literature rely on the use of single classifier and ensemble methods. Single classifier methods vary between them according to different criteria especially the number of formal concepts generated. We distinguish overall complete lattice methods, sub-lattice methods and concept cover methods. Methods based on ensemble classifiers rely on the use of many classifiers. Among these methods, there are methods based on sequential training and methods based on parallel training. However, with the large volume of data generated from various sources, the process of knowledge extraction with traditional methods becomes difficult. That’s why new methods based on distributed classifier have recently appeared. In this paper, we present a survey of many FCA-based approaches for classification by dividing them into methods based on a mono-classifier, methods based on ensemble classifiers and methods based on distributed classifiers. Different methods are presented and compared within this paper.