This study introduces a novel approach to process monitoring by developing a Coefficient of Variation (CV) control chart based on the percentiles of the Size-Biased Lomax Distribution (SBLD). Traditional control charts, such as Shewhart charts, often assume normality, which may not be suitable for skewed or heavy-tailed data commonly found in realworld processes. The proposed CV control chart leverages the SBLD, a distribution well-suited for modeling skewed data, to provide more accurate and robust monitoring of process variability. The methodology involves deriving the mathematical properties of the SBLD, constructing control limits using percentiles, and validating the chart's performance through simulation studies and real-world applications. Results demonstrate that the SBLD-based CV control chart outperforms traditional methods in detecting process variations, particularly for skewed data. This research contributes to the field of statistical process control by offering a tailored solution for monitoring processes with non-normal data distributions, enhancing quality control practices across various industries.