Flare events in industrial processes are undesirable because of their negative economic and environmental impacts. Early warnings of flare events are needed in preventing occurrences of flaring, as corrective manual interventions can be performed by the plant operators. This article reports the successful application of a hierarchically distributed monitoring approach for flare event prediction. The industrial process considered here is a refinery with a flare gas recovery system installed, where flare events need to be monitored in real time. We use various data analysis tools, including principal component analysis, slow feature analysis, and wavelet transform, under a distributed process monitoring framework to achieve monitoring solutions. Results indicate that the application of the proposed hierarchically distributed monitoring framework is able to predict a majority of the flare events, with an acceptable false alarm rate for the data set we studied.