Abstract: This paper analyzes the characteristics of PCs and PLCs, and proposes a method where complex algorithms are implemented on a PC, and the resulting control inputs are transmitted to the PLC, which then performs the required control functions. This approach allows for the easy incorporation of advanced control strategies and algorithms while ensuring reliable and stable control. Taking the application of BP neural network PID control in temperature control as an example, this paper demonstrates the implementation of neural network PID temperature control using a microcontroller data acquisition board interfaced with Matlab, combined with AB's SLC5 microcontroller. Keywords: BP Neural Network, PID, Matlab, data acquisition board Abstract: This paper analyzes the characteristics of PC and PLC, and proposes the idea that PC performs complex operations, and the results are transferred to PLC. PLC completes the control function. It is easy to add advanced control strategies and achieve good results reliably and steadily. For example, the BP NN PID in the temperature control system, through the data collection board which can access Matlab, with AB company SLC5, achieves NN PID control in the temperature control system. Keywords: BP Neural Network, PID, Matlab, data collection board 1. Introduction PLC is widely used in various industrial production and process control, specifically including logic control of switching quantities, motion control, closed-loop process control, etc. PLC is designed specifically for industrial control, with high reliability, strong anti-interference ability, simple programming, convenient use, stability, and low workload in system design, installation, debugging and commissioning. However, the motion control modules of general PLCs are only suitable for simple algorithm control, and are limited for complex and advanced control algorithms. PCs have an open structure, can be loaded with various application software, have complete hardware, flexible interfaces, and can run complex tasks using different software. Currently, in many PC and PLC integrated applications, the PC mainly plays a supervisory and management role, or is used to edit PLC instructions and software operations, while the PLC is mainly used to complete the field control functions. With the continuous development of industrial applications, the requirements for control technology are getting higher and higher, and simple control strategies and algorithms can no longer meet all industrial requirements. Using advanced control strategies and algorithms is the trend and requirement of development. Advanced control methods [1] are adopted, such as model predictive control, internal model control, adaptive control, optimal control, etc., and combined with artificial intelligence algorithms, such as fuzzy control, neural network control, expert control, genetic algorithm, etc., to effectively control complex systems. If these algorithms are implemented through PLC control modules, writing ladder diagram instructions is quite complicated; writing low-level module code, such as neural network control modules, is even more difficult. This paper combines the characteristics of both PCs and PLCs, proposing a method where a complex algorithm is implemented on a PC, and the resulting control input is transmitted to the PLC. The PLC then performs the required control function, making it easy to incorporate advanced control strategies and algorithms while ensuring reliable and stable control. This paper takes the application of BP neural network PID control in temperature control as an example. Using a microcontroller data acquisition board with a Matlab interface, combined with AB's SLC5, neural network PID temperature control was implemented. For details, please click: Application of Neural Networks in PLC Temperature Control Systems