Process control is important for enhancing the quality of manufactured products. Manufacturing processes are generally monitored by observing uniformly sampled process signals collected from application specific sensors and comparing them against known standard patterns. Effective process monitoring and control requires identification of different types of variation, including recurring patterns, in process variables. From the process control view point, any repeating patterns in the process measurements will warrant an investigation into potentially assignable causes. In order to devise an effective process control scheme, a novel universally applicable method for the identification of the repeated occurrence of patterns in process measurements is described in this thesis. First the sampled process signal is decomposed into signals of different resolution using à trous translation invariant wavelet transform. Next, a frequency index is assigned to every sampling point of the process signal at every resolution level to improve the pattern recognition. Recurring patterns detected at different resolutions using neighborhood search in Euclidian space. The experimental results show that the method used in this work accurately detects a broader family of recurring patterns even in the presence of noise.