A typical tutorial system functions by means of interaction between four components: the expert knowledge base component, the inference engine component, the learner's knowledge component and the user interface component. In typical tutorial systems the interaction and the sequence of presentation as well as the mode of evaluation are predetermined and follow a somewhat linear sequence. This model was implemented in many of the early computer based trainings, computer assisted instruction systems and tutorial drill programs. However, by introducing artificial intelligence in the inference engine or by enhancing the expert system component (by means of including feedback), by improving the evaluation of the learners responses and facilitating interaction between these components one may provide a learning environment that more closely resembles a real teacher and student interaction. This approach is known as Intelligent Tutoring Systems (ITS). Various tutorial systems were developed based on this paradigm that proved useful in knowledge domain areas that are highly structured and relatively small (e.g. solving math problems or balancing chemical equations). The difficulty resides in the complexity involved in making the various components encompassing and complete in a knowledge area. For instance, understanding why learners commit a particular error and then assisting them is highly challenging since the cause of an error might be different for every learner. Variations on this ITS model have been employed with success in developing tutoring systems in less structured knowledge domains and more generic environments. Another element that improves ITS functionality is the ability to deliver the correct and necessary granule of material for effective coverage and completion of the knowledge area. The question of where to start a learner in the tutorial system and how to choose the next step is difficult to delineate. In this paper we propose an approach based on the fuzzy set theory to determine the entry knowledge level possessed by a learner in a specific area of learning. Two relations between the knowledge area and the skill levels of a user are established. The first relation is created between the given behavior or knowledge and the mastery of the foundation skills required for it. The second relation is between the given behavior or knowledge and the required exposure to the knowledge domain. The matrices are manipulated to drive a set of values. The resulting values reflect the amount of familiarity of a learner with the knowledge domain. These values can be utilized to provide an accurate starting place for the delivery of a training set or curriculum in a given domain of knowledge. Continuous evaluation and determination of the values can also be utilized to determine the granule of materials to be delivered for the most efficient progress through the knowledge area. (Contains 3 figures.)