The main aim of this study is to estimate the bearing strength on pin loaded composite plates using test data of different ambient conditions depending on time. Artificial neural network (ANN) tool is used for prediction purpose. Artificial neural network program is developed by MATLAB software. The composite plates were divided into nine groups, each one contains 60 specimens. These groups were kept in different environment conditions. To obtain the optimum geometrical dimensions of specimens, the distance from hole axis to the side of the test sample of the pin diameter ratio (E/D) and the sample width to pin diameter ratio (W/D) were changed systematically in the experimental samples. Data from fatigue test results obtained from the multi-layered, feedforward and backpropagation algorithm were used to train the artificial neural network model. In modeling of artificial neural network, the test conditions, the test periods, the day intervals, the distance from hole axis to the side of test sample of the pin diameter ratio (E/D) and the sample width to pin diameter ratio (W/D) were used as input parameters and the bearing strength data was used as output parameter. The values obtained from the artificial neural network training and testing were evaluated by applying statistical analyses that are widely used in ANN models. It is widely known that difficult experimental studies and complexity of the analytical expression could be solved by ANN models as it was seen in many studies. This study has also shown that artificial neural network is a convenient method for predicting the bearing strength on pin loaded composite plates.