Modern computerized monitoring and control systems in chemical plants result in vast amount of data being collected daily. This data contains valuable information about the process and can aid in effective decision making. Hence it has to be stored without distortion of the underlying trends, using effective data compression techniques, such that it can be readily accessible upon request. In this work, a dyadic B-Splines based data compression algorithm;has been developed, which achieves data compression by denoising (removing noise) the data belonging to each sensor. This denoised and compressed data, in addition to being archived in a historical data base, is used in trend based process monitoring and diagnosis. The process monitoring algorithm can detect abnormal frequencies, identify changes in correlation among sensor variables and perform foot cause analysis, thus reducing the number of alarms produced during abnormal situations. The application of the data compression algorithm and process monitoring algorithm is demonstrated on the data from a furnace in a crude distillation unit.