Here, we report how neural networks can be configured such that strand is predicted significantly better, and that the prediction looks like native proteins in terms of the length of predicted segments. Using evolutionary information contained in multiple sequence alignments as input to neural networks, secondary structure can be predicted at significantly increased accuracy. Pre-processing the alignment information by using a position-specific conservation weight and the number of insertions and deletions in each alignment position is found to be advantageous. Addition of the global amino acid content yields a further improvement, mainly in predicting structural class. The final network system has a sustained overall accuracy of more than 72% evaluated on 250 sequence-unique chains. Of particular practical importance is the definition of a position-specific reliability index. For 40% of all residues the method has a sustained three-state accuracy of 88%, as high as the overall average for homology modelling.