In the semiconductor industry, run-to-run (R2R) control is an important technique to improve process capability and further enhance the production yield. As the dimension of electronic device shrinks increasingly, wafer-to-wafer (W2W) advanced process control (APC) becomes essential for critical stages. W2W APC needs to obtain the metrology value of each wafer; however, it will be highly time and cost consuming for obtaining actual metrology value of each wafer by physical measurement. Recently, an efficient and cost-effective approach denoted virtual metrology (VM) was proposed to substitute the actual metrology. To implement VM in W2W APC, both conjecture-accuracy and real-time requirements need to be considered. In this paper, various VM algorithms of back-propagation neural network (BPNN), simple recurrent neural network (SRNN) and multiple regression (MR) are evaluated to see whether they can meet the accuracy and real-time requirements of W2W APC or not. The fifth-generation TFT-LCD CVD process is used to test and verify the requirements. Test results show that both one-hidden-layered BPNN and SRNN VM algorithms can achieve acceptable conjecture accuracy and meet the real-time requirements of semiconductor and TFT-LCD W2W APC applications.