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This paper presents a wavelet transform-based data fusion algorithm for multisensor systems. With this algorithm, the optimum estimate of a measurand can be obtained in terms of minimum mean square error (MMSE). The variance of the optimum estimate is not only smaller than that of each observation sequence but also smaller than the arithmetic average estimate. To implement this algorithm, the variance of each observation sequence is estimated using the wavelet transform, and the optimum weighting factor to each observation is obtained accordingly. Since the variance of each observation sequence is estimated only from its most recent data of a predetermined length, the algorithm is self-adaptive. This algorithm is applicable to both static and dynamic systems including time-invariant and time-varying processes. The effectiveness of the algorithm is demonstrated using a piecewise-smooth signal and an actual time-varying flow signal.