•Two automated defect detection algorithms are established, based on the correlation coefficient and Boolean functions.•Both algorithms are efficiently implemented to detect defects in printed circuit boards.•Detailed performance and comparisons with recent relevant techniques are carried out in normal and presence of noise situations. We establish two automated defect detection (ADD) algorithms and apply them to detect faults in printed circuit boards (PCBs). Both techniques are referential and are implemented on the binary bit-plane images of the PCBs. In the first algorithm we measure the association between reference and inspected images via the φ-correlation coefficient of percentage statistics, while in the second approach we apply a carefully selected boolean function, as well as a smoothing median filter. Both techniques show a high accuracy, which is comparable to the state-of-the-art techniques, but in a notably faster time. For instance, the boolean function approach is faster than the use of the normalized cross correlation (NCC) by 1700% and it is faster than the use of a fast form of the NCC by 700%, while the use of the φ-correlation coefficient accelerates the procedure by 500% and 200% for the use of the NCC and some of its fast forms respectively.