A human action recognition method based on a hidden Markov model (HMM) is proposed. It is a feature-based bottom-up approach that is characterized by its learning capability and time-scale invariability. To apply HMMs, one set of time-sequential images is transformed into an image feature vector sequence, and the sequence is converted into a symbol sequence by vector quantization. In learning human action categories, the parameters of the HMMs, one per category, are optimized so as to best describe the training sequences from the category. To recognize an observed sequence, the HMM which best matches the sequence is chosen. Experimental results for real time-sequential images of sports scenes show recognition rates higher than 90%. The recognition rate is improved by increasing the number of people used to generate the training data, indicating the possibility of establishing a person-independent action recognizer.