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This paper presents a performance analysis of three categories of adaptive filtering algorithms in the application of linear prediction. The classes of algorithms considered are Least-Mean-Square (LMS) based, Recursive Least-Squares (RLS) based and Lattice based adaptive filtering algorithms. The performances of the algorithms in each class are compared in terms of convergence behavior, execution time and filter length. The analysis determines the best converging algorithm from each class. Finally the best performing algorithm for adaptive linear prediction is selected.