Catastrophic forgetting occurs when a neural network loses its ability to perform a previously learned task after being fine-tuned on a new task, because new training overwrites the weights encoding original knowledge. This is a major challenge in continual learning. Techniques like elastic weight consolidation and replay buffers help mitigate it by preserving important weights during new training.