This paper reports on a pioneer effort for the establishment of a software composite metric withkey capability of distinguishing among different structures. As a part of this effort most of the previouslyproposed program control-flow complexity metrics are evaluated. It is observed that most of these metricsare inherently limited in distinguishing capability. However, the concept of composite metrics is potentiallyuseful for the development of a practical metrics. This paper presents a methodology for the develop-ment of a practical composite metric using statistical techniques. The proposed metric differs from allprevious metrics in 2 ways: (1) It is based on an overall structural analysis of a given program indeeper and broader context. It captures various structural measurements taken from all existingstructural levels; (2) It unifies a set of 19 important structural metrics. The compositing model of thesemetrics is based on statistical techniques rather than on an arbitrary method. Experinces with the pro-posed metric clearly indicate that it distinguishes different structures better than the previous metrics. This paper reports on a pioneer effort for the establishment of a software composite metric with key capability of distinguishing among other structures. As a part of this effort most of the previouslyproposed program control-flow complexity metrics are evaluated. It is observed that most of these metricsare However, the concept of composite metrics is potentially useful for the development of a practical metrics. This paper presents a methodology for the develop-ment of a practical composite metric using statistical techniques. The proposed metric differs from all previous metrics in It captures various structural measurements taken from all existing structural structural levels. (2) It is based on an overall structural analysis of a given program indeeper and broader context. of thesemetrics is based on statistical techniques rather than on an arbitrary method. Experinces with the pro-posed metric clearly indicate that it distinguishes different structures better than the previous metrics.