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The automatic identification and analysis of pronunciation errors, known as mispronunciation detection and diagnosis (MDD), is vital in computer-aided pronunciation learning (CAPL) tools for second-language (L2) learning. Existing MDD methods focus on analyzing phonemes, but they can only detect categorical errors for phonemes with sufficient training data. Due to the unpredictable nature of non-native speakers’ pronunciation errors and limited training datasets, modelling all mispronunciations becomes impractical. Additionally, phoneme-level MDD approaches provide limited diagnostic information. In our proposed approach, we detect phonological features, breaking down phoneme production into elementary components related to the articulatory system, offering more informative feedback to learners. Applied to L2 English speech data, it outperformed traditional phoneme-level methods, reducing false acceptance rate (FAR), false rejection rate (FRR), and diagnostic error rate (DER).