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According to the characteristics of data measured from refrigeration equipment in low temperature test system, such as a huge number of points, a strong correlation between the data, genetic neural network combined with principal component analysis (PCA) is introduced into fault diagnosis in the refrigeration system. With the knowledge of expert experience and PCA, the fault feature is extracted from multi sensor information in a scientific and reasonable way, so the input space of the neural network is fixed. The defects of neural network is easy to fall into the minimum in local space, but genetic algorithm(GA) has global search ability, aim at eliminating the defects, GA is used to optimize the initial weights and thresholds of neural network. Using the method into the fault state identification of the refrigeration system, it showed that the simple and effective network structure not only shorten the training time, but also improve the network stability and classification accuracy, so it provides an effective method of fault diagnosis for the monitoring system.