Recurrent Neural Networks (RNNs) are a class of artificial neural networks designed to recognize patterns in sequences of data, such as time series or natural language, by utilizing their internal memory to process inputs of variable lengths. They are particularly well-suited for tasks where context and sequential information are crucial, but they can struggle with long-term dependencies due to issues like vanishing gradients.