Variation source identification for manufacturing processes is critical for product dimensional quality improvement, and various techniques have been developed in recent years. Most existing variation source identification techniques are based on a linear fault-quality model, in which the relationships between process faults and product dimensional quality measurements are linear. In practice, many dimensional measurements are actually nonlinearly related to the process faults: For example, relational dimension measurements such as the relative distance between features are used to monitor composite tolerances. This paper presents a variation source identification methodology in the presence of these relational dimension measurements. In the proposed methodology, the joint probability density of the measurements is determined as a function of the process parameters; then, series of statistical comparisons are performed to differentiate and identify the variation source. A case study is also presented to illustrate the effectiveness of the methodology.