XOR problem is not linearly separable and makes single layer perceptron unfit. However, in your network addition of hidden layer makes the network to capture non-linear features, which makes it fine.
The most plausible reason for the poor performance of the network would be due to tortuous initial phase to learn the problem. So increasing the iterations would solve the problem.
And one more possible thing to try is by the smooth nonlinearity of XOR, so the role of bias is crucial as the translation parameter and as important as weights (which you did not mention)
