Bioinformatics is a multi-disciplinary field that applies principles from Mathematics, Physics, Chemistry and Computer Science to widespread, numerous and complex biological data. The aim of bioinformatics is to solve biological problems in molecular level. Proteins are the basic functional units of living organism’s cells which carry out almost all the activities of life. Each protein molecule comprises of an amino acid chain. Four structures are defined for protein, namely primary, secondary, tertiary (3-D) and quaternary. Primary structure is the amino acid chain. Secondary structures are local structures which are generated by hydrogen bonds. The most common types of the secondary structures are alpha helices and beta sheets. Tertiary structure is the final shape of a protein molecule which is formed by folding amino acid chain. In fact, it is the spatial status of the secondary structures in relation to eachother. The quaternary structure is formed by collection of several proteins. Protein function is dependent on its tertiary structure. The determination of the 3-D structure of proteins, is an important step toward understanding the behavior of them. Tertiary structure itself is dependent on amino acid chain. The determination of tertiary structure is not as straightforward process as the primary one. Existing experimental processes to determine tertiary structure are costly and time-consuming which encourage researchers to find methods to predict protein tertiary structure only based on its amino acid chain. Contact map prediction is one of such method. Protein contact map is a simplified, 2-D representation of protein spatial structure. The purpose of contact map prediction problem is to compute an estimate of contact map of a protein based on its primary structure and features that are computable or predictable from primary structure. Over the years, a variety of statistical and machine learning methods have been developed to predict contact map. Committee machine is a machine learning method which divides the learning task among a number of learners and input spaces into some sub-spaces. Learner’s responses to an input, are combined to produce the system’s final response that is more accurate than of every individual’s response. The aim of this research is to propose a novel method for contact map prediction based on committee machine. In the proposed method, learner group is a set of neural networks. Different features are extracted and then in two phases, the learner group is generated as predictive model. The important principle in evaluating contact map prediction is the ratio of correct predicted contacts to all predicted ones. To analyze the results of the proposed model, two o Key Words: Bioinformatics, Machine Learning, Committee Machine, Neural Network, Protein Contact Map, Contact Map Prediction