Neural and neuro-fuzzy systems are used, in order to forecast temperature and solar radiation. The main advantage of these systems is that they don’t require any prior knowledge of the characteristics of the input time-series in order to predict their future values. These systems with different architectures have been trained using as input-data measurements of the above meteorological parameters obtained from the National Observatory of Athens. After having simulated many different structures of neural networks and trained using measurements as training data, the best structures are selected in order to evaluate their performance in relation with the performance of a neuro-fuzzy system. As the alternative system, ANFIS neurofuzzy system is  considered, because it combines fuzzy logic and neural network techniques that are used in order to gain more efficiency. ANFIS is also trained with the same data. The comparison and the evaluation of both of the systems are done according to their predictions, using several error metrics.