This paper introduces a new application of Bollinger bands for defect detection of patterned fabric. A literature review on previous designed methods for patterned fabric defect detection will be depicted. For data analysis, Bollinger bands are calculated based on standard deviation and are originally used in the financial market as an oversold or overbought indicator for stock. The Bollinger bands method is an efficient, fast and shift-invariant approach, that can segment out the defective regions on the patterned fabric with clear and crystal clean images. The new approach is immune of the alignment problem that often happens in previous methods. In this paper, the upper band and lower band of Bollinger bands, which are sensitive to any subtle change in the input data, have been developed for use to indicate the defective areas in patterned fabric. The number of standard deviation and length of time of Bollinger bands can be easily determined to obtain excellent detection results. The proposed method has been evaluated on three different patterned fabrics. In total, 165 defect-free and 171 defective images have been used in the evaluation, where 98.59% accuracy on inspection has been achieved.