A hidden Markov model (HMM) is a statistical tool used to represent systems that are assumed to follow a Markov process with hidden states. In this model, the system transitions between different states over time, but these states are not directly observable. Instead, we can only see outputs or observations that are influenced by these hidden states. HMMs are widely used in various fields, including speech recognition, bioinformatics, and finance. They help in predicting future states based on past observations, making them valuable for tasks like language modeling and sequence analysis.