The principal object of this research is to develop a neural network model, which can simulate the solder paste thickness process in Surface Mounting Technology (SMT).Then the Boolean logic rules were extracted from the trained network model to establish a knowledge base of the expert system. Here the input data of neural network was collected from the process parameters of printer machine (DEK) in the SMT department, which included the pressure and speed of the printing, distance and speed of the separation, frequency and mold of the cleaning. The inspecting values from the Auto Optical Inspection (AOI) were adopted as the output. The neural network model was constructed with back-propagation network (BPN) algorithm and trained 500 times with 400 training data. The model was tested with 65 test data and the error rate was ±2.24% in average. The extraction rules were obtained from the trained neural network with the rule extraction algorithm to describe the knowledge in the network system with logic rules. After checking, explaining and integrating the rules into the knowledge base, the rules can be the basics of solder paste thickness prediction and alarm diagnosis in print process system. When the parameters are abnormal, to make the solder paste thickness out of control limit, an alert message can be offered and also the knowledge base can provide a reference to the engineers for the work of recipe adjustments.