Protein secondary structure prediction is believed to improve by combining different predictions into a consensus secondary structure prediction. Ten different protein secondary structure prediction programs were compared and given weights by a feed forward neural network. A dataset of approximately 6000 proteins was taken from the DSSP database and was used to train the neural network. The resulting weights indicate that the secondary structure prediction programs PHD and Predator performed better than the other methods. However training of the neural network with a smaller but more stringently selected dataset did not support these results for the Predator program. The performance of the program PHD remained the same when the smaller dataset was used to train the neural network.