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The search for pre‐eruptive observables that can be used for short‐term volcanic forecast remains a scientific challenge. Pre‐eruptive patterns in seismic data are usually identified by analyzing seismic catalogs (e.g., the number and types of recorded seismic events), the evolution of seismic energy, or changes in the tensional state of the volcanic medium as a consequence of changes in the volume of the volcano. However, although successful volcanic predictions have been achieved, there is still no generally valid model suitable for a large range of eruptive scenarios. In this study, we evaluate the potential use of Shannon entropy as short‐term volcanic eruption forecasting extracted from seismic signals at five well studied volcanoes (Etna, Mount St. Helens, Kilauea, Augustine, and Bezymianny). We identified temporal patterns that can be monitored as short‐term eruptive precursors. We quantified the decay of Shannon entropy prior to eruptions, noting that changes appear between 4 days and 12 hr before. When Shannon entropy is combined with the temporal evolution of other features (i.e., energy, kurtosis, and the frequency index), we can elaborate physical models according to the occurring volcanic processes. Our results show that pre‐eruptive variation in Shannon entropy is a confident short‐term volcanic eruption monitoring tool. Plain Language Summary: Volcanic eruptions represent a major natural hazard. Despite decades of research, the forecasting of volcanic eruptions remains a scientific challenge. Subsurface volcanic processes generate seismic waves, which can be measured at the surface using seismometers. To date, the most successful examples of eruption forecasting have been based on seismic data. However, we still lack a early warning model that can be applied across the wide range of eruption styles seen around the world. In this study, we implemented a new approach for the analysis of seismo‐volcanic data aimed at warning eruptions. We used advanced signal processing algorithms to analyze continuous seismic signals from a suite of well‐studied volcanoes (Mount St. Helens, Mt. Etna, Kilauea, Augustine, and Bezymianny) in order to create a new database of features found within the seismic signals. We found that pre‐eruptive variation in the Shannon entropy (a statistical parameter associated to the coherence of the seismic sources) of seismic signals offers a successfully feature for short‐term volcanic eruption alert. The relationship between pre‐eruptive seismic signals and Shannon entropy is based on changes in the probability distributions of the type of seismic waves, independent of the signal source. Key Points: While successful volcanic forecasting has been achieved, there is no generally valid model suitable for large range of eruptive scenariosWe used feature extraction to analyze seismic data from five well studied volcanoes to identify short‐term eruptive precursorsShannon entropy has a uniform temporal pattern of pre‐eruptive change and is a recurrent and transferable feature [ABSTRACT FROM AUTHOR]