Growing amount of information in the world encourage the use of automatic data processing techniques that reduce humans routine work. There is a wide range of methods used for machine learning; however inductive learning algorithms are preferable in the systems where understanding of decision making steps and further processing of results is needed, for instance the expert systems, where the rules induced by learning algorithms can be used. As the classification tasks are getting more complicated computer program may not make enough informed decision by itself. In such situations collaborative approach between machine and systems user (expert) would be useful. Inductive learning system learns classification from training examples and uses induced rules for classifying new cases. If a decision cannot be inferred from rules base, a guess is performed. Interactive inductive system in uncertain conditions could ask human for decision and improve its knowledge base with the rule derived from this human-made decision. The paper summarises approaches discussed in related works and classifies them by the phase in inductive learning process in which the human interaction appears. As a result a new approach to interactive inductive system is presented. Conceptual example of topographical map classification using this system is demonstrated.