This work presents the development of an artificial neural network model based on the Multi-layer Perceptron (MLP) and Radial Basis Function (RBF) for predicting the moisture in the zone of Chefchaouen (Morocco). Our objective is to treat a chronological series of measured data for network response evaluation. For this reason, we used a basic learning neural model for the years 2008 and 2013, the latter consists of a number of meteorological parameters such as humidity, temperature of the air, dew point temperature, air pressure, visibility, cloud cover, wind speed and rain. To determine the network architecture to be used, we varied the number of hidden number of neurons in hidden layers, transfer functions and pairs of transfer functions and learning algorithms. Models performances have been evaluated and developed through the study of the Mean Squared Error (MSE) and correlation coefficient (R). We demonstrated in this study for the prediction of moisture, the best performing model is the one used as transfer functions, the Tansig function in the hidden layer and the Purelin function in the output layer, while using a learning algorithm LM, PMC type of configuration.