The hybrid system based on the stochastic and wavelet analyses was applied to a large amount of data from an actual yarn manufacturing process, and the performance was verified by applying the system developed. Ten yarn packages were measured on a Shlafhors $t_{R}$ open-end spinning machine at normal production conditions, providing data for a total of length of 250,000 m, or 25,000 m for each package. The massive amount of data could be reduced to a small fraction consisting of i) a set of statistical parameters for normal sub-blocks, ii) sets of statistical parameters on abnormal sub-blocks for characterizing yam and fabric defects and for providing their timedomain addresses, and iii) wavelet coefficients for abnormal sub-blocks. The reduction rate was approximately 99.9999% in the example at which 100 spindles could be monitored for 10 hours by storing only a small fraction of the data.a.