An integrated approach for outlier detection and data reconciliation is discussed. It is shown that outliers can be identified by directly examining the measurement distribution. In our approach, a non-linear limiting transformation which operates on the data set is utilised to eliminate or reduce the influence of outliers on the performance of the conventional data reconciliation. Monte Carlo study shows that the proposed approach provides better results than the conventional approach in the presence of outliers. When no outlier exists, it provides as good a performance as the conventional approach.