In this paper we discuss a neural network model that can recognize patterns rotated at various angles. The model employs copy learning, a learning method entirely different from those used in conventional models. Copy-Learning is an effective learning method to attain the desired objective in a short period of time by making a copy of the result of basic learning through the application of certain rules. Our model using this method is capable of recognizing patterns rotated at various angles without requiring mathematical preprocessing. It involves two processes: first, it learns only the standard patterns by using part of the network. Then, it copies the result of the learning to the unused part of the network and thereby recognizes unknown input patterns by using all parts of the network. The model has merits over the conventional models in that it substantially reduces the time required for learning and recognition and can also recognize the rotation angle of the input pattern.