The network is usually trained with a large number of input-output pairs.1. Generate weights randomly to small random values (both positive and negative) to ensure that the network is not saturated by large values of weights.2. Choose a training pair from the training set.3. Apply the input vector to network input.4. Calculate the network output.5. Calculate the error, the difference between the network output and the desired output.6. Adjust the weights of the network in a way that minimizes this error.7. Repeat steps 2 - 6 for each pair of input-output in the training set until the error for the entire system is acceptably low.