In a supervised learning, the relationship between the available data and the performance (what is learnt) is not well understood. How much data to use, or when to stop the learning process, are the key questions.

In the paper, we present an approach for an early assessment of the extracted knowledge (classification models) in the terms of performance (accuracy). The key questions are answered by detecting the point of convergence, i.e., where the classification model's performance does not improve any more even when adding more data items to the learning set. For the learning process termination criteria we developed a set of equations for detection of the convergence that follow the basic principles of the learning curve. The developed solution was evaluated on real datasets. The results of the experiment prove that the solution is well-designed: the learning process stopping criteria are not subjected to local variance and the convergence is detected where it actually has occurred.