MLPs consist of multiple layers of interconnected neurons, including an input layer, one or more hidden layers, and an output layer. The input layer receives the input data, which is then processed through the hidden layers using a set of weights and biases. The output layer produces the final output, which is compared to the desired output to calculate the error. The back propagation algorithm is then used to adjust the weights and biases in the network to minimize the error and improve the accuracy of the model.

MLPs have been successfully applied in various fields, including image and speech recognition, natural language processing, financial forecasting, and medical diagnosis. They are also commonly used in deep learning architectures, such as convolutional neural networks and recurrent neural networks, to improve their performance.

However, MLPs have some limitations, such as the need for large amounts of training data, the risk of overfitting, and the difficulty in interpreting the learned features. Researchers are continuously working on developing new techniques to overcome these challenges and improve the performance of MLPs.