Abstract As deregulation and new competitors open up the telecommunications industry, the cellular phone market has become more competitive than ever. To survive or maintain an advantage in such a competitive marketplace, many telecommunications companies are turning to data mining techniques to resolve such challenging issues as fraud detection, customer retention, and prospect profiling. In this thesis, we focused on developing and applying data mining technique to support the churn prediction. Constrained by limited customer profiles and general demographics, the proposed approach applied a decision tree induction technique (i.e., C4.5) to discover a classification model for churn predication solely based on the call records. To deal with the training data with a highly skewed distribution on decisions (i.e., around 2% churners and 98% non-churners), a multi-expert strategy was adopted. The empirical results showed that the proposed technique was effective in predicting at-risk cellular phone customers (i.e., potential churners). The proposed technique could identify 50.64% churners by selecting 10.03% of the population, and 68.62% churners by selecting 29.00% of the population.