This paper presents a theoretical framework for developing a suite of metrics to evaluate object-oriented design (OOD). The metrics are grounded in measurement theory and informed by insights from experienced object-oriented software developers. The authors evaluate these metrics against seven standard criteria for software metrics, finding that they perform relatively well and suggest differences between OOD and traditional approaches. The paper introduces six candidate metrics: Weighted Methods Per Class (WMC), Depth of Inheritance Tree (DIT), Number of Children (NOC), Coupling Between Objects (CBO), Response For a Class (RFC), and Lack of Cohesion in Methods (LCOM). Each metric is defined and evaluated based on its theoretical basis and viewpoints. The results show that all six metrics fail to meet certain properties, particularly permutation significance and monotonicity, indicating the need for further research and refinement. The paper concludes by outlining future empirical research plans to validate the metrics on actual systems and explore their predictive power for managerial performance indicators.This paper presents a theoretical framework for developing a suite of metrics to evaluate object-oriented design (OOD). The metrics are grounded in measurement theory and informed by insights from experienced object-oriented software developers. The authors evaluate these metrics against seven standard criteria for software metrics, finding that they perform relatively well and suggest differences between OOD and traditional approaches. The paper introduces six candidate metrics: Weighted Methods Per Class (WMC), Depth of Inheritance Tree (DIT), Number of Children (NOC), Coupling Between Objects (CBO), Response For a Class (RFC), and Lack of Cohesion in Methods (LCOM). Each metric is defined and evaluated based on its theoretical basis and viewpoints. The results show that all six metrics fail to meet certain properties, particularly permutation significance and monotonicity, indicating the need for further research and refinement. The paper concludes by outlining future empirical research plans to validate the metrics on actual systems and explore their predictive power for managerial performance indicators.