This paper examines the problem of redundancy reducing of the telemetry data related to space objects. Telemetry data are redundant in most cases due to their pattern-like nature. Therefore, one can store information concerning patterns occurred and ways of data reconstruction instead of the large volumes of raw data. At the same time, discrete wavelet transforms proved to be an efficient way of detecting patterns and features of digital signals and thus possess significant redundancy reducing capabilities. This study analyzes the redundancy reducing capabilities of discrete wavelet transforms applied to various telemetry signals acquired from space satellites and uses the criterion of minimum entropy of the detail coefficients for the estimation. The results show that some of the Daubechies and biorthogonal discrete wavelet transforms are an efficient option for redundancy reducing for one group of tested signals but at the same time, they fail to reconstruct signals from another group with the acceptable level of accuracy. The findings also show high signal reconstruction accuracy of recently proposed symmetric ternary discrete wavelet transform. Conclusions consist of recommendations for prioritizing certain discrete wavelet transforms for the tasks of redundancy reducing depending on the reconstruction error thresholds and open the floor for further studies.