The most common approach to seismic triggering is to compare short‐term averages (STA) with long‐term averages (LTA) of transformed amplitudes. In recording environments where this technique is of limited use, hidden Markov models (HMMs) are increasingly used for statistical event detection and classification, but these require training data and are often susceptible to false positive detection errors. In this work, we introduce an adaptive STA–LTA triggering algorithm that uses STA and LTA of state probabilities defined by restricting an HMM to a two population model of outliers in background noise. Monte Carlo simulations of noise and synthetic events are used to investigate detector sensitivity using statistical properties of latent states. We compare our method with traditional STA–LTA triggering on real data recorded by a 12‐station vertical borehole array near Hoadley gas field, Alberta, Canada. These tests suggest that our method is more accurate when dealing with closely spaced events and is less susceptible to false positive detection errors. When existing picking algorithms are adapted for HMMSTA–LTA, the result is improvement in total picks, accuracy, and consistency. A narrow range of detection thresholds is optimal for a wide range of signal‐to‐noise ratios; this suggests HMMSTA–LTA may be less sensitive to analyst parameter choices than even traditional STA–LTA.