This paper presents a feature ranking scheme based on sparse distance correlation (SpaDC) to promote diversity and assess general dependency between process features and quality variables. The SpaDC method addresses the complexity and redundancy in manufacturing data, providing a robust tool for identifying leading features in process analysis.