One important aspect of effective real time process management is the implementation of intelligent systems that can assist human operators in making supervisory control decisions. Conventional practice is to simply sound alarms when process variables go out of range leaving it to the operator to interpret the alarm patterns and choose mitigation strategies. Failure of the operator to exercise the appropriate mitigation actions often has an adverse effect on product quality, process safety, and the environment. The difficulties associated with implementing intelligent control and the opportunities for improvements are even greater in the pharmaceutical manufacturing of solid oral dosage products due to specific processing challenges associated with particulate and granular systems. The advent of the Process Analytical Technology (PAT) initiative, developed by the FDA has given the pharmaceutical industry an opportunity to apply systems engineering tools that encourage innovation in drug manufacturing. In this work, an intelligent alarm system (IAS), framework has been developed to deal with fault detection, diagnosis, and Mitigation of conditions that result from process anomalies. The integrated framework, using wavelet analysis, principal component analysis, signed directed graphs, and qualitative trend analysis, along with ontological based knowledge framework, helps in quick detection and diagnosis of process faults. This reduces the likelihood of abnormal event progression, production disruptions, and productivity losses. The key feature of this framework is that it provides a mitigation strategy to the operator along with rationalized alarm thresholds, which help in reducing the workload and facilitate in taking corrective action.