This paper presents the results of surveying the effect of backpropagation network structures on the predictive quality of CBN-coated cutting tools in high-speed dry turning of SKD11 steel on CNC machines. Based on the analysis of the back-propagation network (BPN) characteristics and determining that the number of hidden layers of this network structure is fixed, eighteen network structures corresponding to the six ratios of the number of neurons between the hidden layers are investigated and evaluated. The network training dataset is collected from 280 high-speed turning experiments with four input variables and one output variable. The network quality evaluation criteria include R2, MSE, RMSE and MAPE indexes. The survey results show that, in this particular case study, the ratio of the number of neurons between hidden layer 1 and hidden layer 2 reaches 1:2, giving the best prediction quality. The 4-10-20-1 network configuration is the model for the best quality. The research results can serve as a basis for selecting the appropriate neural network (NN) configuration for models with large amounts of data and many input variables. However, this study only examines the network model with 2 hidden layers.