In the work, two multivariate analysis methods Neural Network (NN) and Boosted Decision Tree (BDT) were used to separate the ZH(bb ̅) signal from the background and the results obtained from them were compared. The list of input variables for BDT and NN is similar to those used in the analysis in the ATLAS experiment. Up to 0.8 million signals and the same number of background events were used for training and testing. The settings used in the ATLAS analysis, which has the best performance, were chosen to tune the BDT hyperparameters. Various number of events (0.1M, 0.2M and 0.8M) are trained and different settings for NN are obtained, providing performance that exceeds that of BDT. It turns out that for any number of training events, it is possible to find corresponding NN settings with better performance than BDT. The problem with NN training is that it is computationally intensive compared to BDT.