Plant data are used to compare the effectiveness of wavelet-based methods with other compression techniques. The challenge is to effectively treat the data so that the maximum compression ratio is achieved while the important features are retained in the compressed data. Wavelets have properties that are desirable for data compression. They are localized in time (or space) and in frequency. This means that important short-lived high-frequency disturbances can be preserved in the compressed data, and these disturbances may be differentiated from slower, low-frequency trends. Besides discrete wavelet transforms, linear interpolation, discrete cosine transform, and vector quantization are also used to compress data. The transform-based compression algorithms perform better than the linear interpolation methods, such as swinging door, that have been used traditionally in the chemical process industries. Among these techniques, the wavelet-based one compresses the process data with excellent overall and best local accuracy.