Arduino UNO is used to generate the thermal profile for the unit under test.Histogram features are extracted from IR images which are used as inputs into a classifier model.The three soft computing techniques; MLP, SVM, and ANFIS, are used to locate the faulty IC.The comparison results concluded that the ANFIS model achieved less number of epochs through training phase and it found the nearest class values. The goal of this paper is to develop a tool that will aid manufacturers of Printed Circuit Boards (PCBs) in testing their production lines with an acceptable rate of fault detection and minimize the test time. The PCB unit, namely the Arduino UNO board, is used to generate the thermal profile for the unit under test. This work is based on using a classification approach that classifies the PCB defects into the Integrated Circuit (IC) level. In the proposed technique, histogram features are extracted from the ICs hotspots which are used as inputs into a classifier model. The number of effective features are minimized by the principal component analysis. The image classification and detection are performed based on three soft computing techniques; multilayer perceptron, support vector machine, and adaptive neuron-fuzzy inference system. The effectiveness of the models is evaluated by comparing their performance and accuracy of classification. Display Omitted