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Performance management and dependability are two of the fundamental issues in business-critical applications. The ability to detect the occurrence of performance failures and anomalies has raised the attention of researchers in the last years. It is in fact a difficult problem, since a visible change in the performance can result from some natural cause (e.g., workload variations, upgrades) or by some internal anomaly or fault that may end up in a performance failure or application crash. Distinguish between the two scenarios is the goal of the framework presented in this paper. Our framework is targeted for web-based and component-based applications. It makes use of AOP-based monitoring, data correlation techniques and time-series alignment algorithms to spot the occurrence of performance anomalies avoiding false alarms due to workload variations. The paper includes some experimental results that show the effectiveness of our techniques under the occurrence of dynamic workloads and some fault-load situations.