Prediction of protein secondary structure is an important step towards 
elucidating its three dimensional structure and its function. This is a 
challenging problem in bioinformatics. Segmental semi Markov models (SSMMs) are 
one of the best studied methods in this field. However, incorporating 
evolutionary information to these methods is somewhat difficult. On the other 
hand, the systems of multiple neural networks (NNs) are powerful tools for 
multi-class pattern classification which can easily be applied to take these 
sorts of information into account. To overcome the weakness of SSMMs in 
prediction, in this work we consider a SSMM as a decision function on outputs of 
three NNs that uses multiple sequence alignment profiles. We consider four types 
of observations for outputs of a neural network. Then profile table related to 
each sequence is reduced to a sequence of four observations. In order to predict 
secondary structure of each amino acid we need to consider a decision function. 
We use an SSMM on outputs of three neural networks. The proposed SSMM has 
discriminative power and weights over different dependency models for outputs of 
neural networks. The results show that the accuracy of our model in predictions, 
particularly for strands, is considerably increased.