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Enterprise systems are load tested for every added feature, software updates and periodic maintenance to ensure that the performance demands on system quality, availability and responsiveness are met. In current practice, performance analysts manually analyze load test data to identify the components that are responsible for performance deviations. This process is time consuming and error prone due to the large volume of performance counter data collected during monitoring, the limited operational knowledge of analyst about all the subsystem involved and their complex interactions and the unavailability of up-to-date documentation in the rapidly evolving enterprise. In this paper, we present an automated approach based on a robust statistical technique, Principal Component Analysis (PCA) to identify subsystems that show performance deviations in load tests. A case study on load test data of a large enterprise application shows that our approach do not require any instrumentation or domain knowledge to operate, scales well to large industrial system, generate few false positives (89% average precision) and detects performance deviations among subsystems in limited time.