Regime types and transitions are central to a wide range of political phenomena. Reflecting this importance, prior research has produced a variety of regime measures. This diversity, however, comes with important challenges for applied research: selecting a measure among many options, having to define regime categories based on cut-offs, identifying regime transitions by specific magnitudes of change over a specific time window, and dealing with measurement uncertainty and missing data. In this paper, we introduce a new approach that offers a solution to these challenges. Combining information from commonly used regime indicators, our approach identifies regime types and transitions probabilistically, locates the most likely periods of regime transitions, and incorporates measurement uncertainty. Through Monte Carlo simulations, we demonstrate its desirable properties and robustness under various scenarios.  In an illustrative application, we show that democratization is not a source of civil war, and semi-democracies may not be as conflict-prone as previously claimed.