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There are numerous fault management techniques that can be used to enhance the reliability of a real-time critical system. Fault masking is one of the significant techniques amid those. It is one of the key approaches to improve or maintain the normal behaviour of a system in the appearance of fault. Voting as fault masking method involves the derivation of an output data object from a collection of n input data objects, as prescribed by the requirements and constraints of a voting algorithm. In data fusion, voting is a probable method of combining different data delivered by several sources (e.g. sensors) whose outputs may be mistaken. This paper proposes a new concept of classifying voting algorithms with some new parameters of evaluation. Then using this new concept and some widely known voting algorithms, we propose a hybrid history based weighted voting algorithm. We show that the proposed algorithm gives better and stable performance, in different error ranges, compared to the existing standard voting algorithms.