The objective of this paper is to introduce the use of two different recurrent neural networks in human hand gesture recognition for static images. Because neural networks are a promising tool for many human computer interaction applications, this paper focuses on the ability of neural networks to assist in Arabic Sign Language(ArSL) hand gesture recognition. We have introduced the steps of our proposed system and have presented the Elmanpsilas model as a partially recurrent architecture and a fully connected network with recurrent links that is believed to help the network to converge and gain stability, then we have tested it in an experiment held for this; the results of the experiment have showed that the suggested system with the fully recurrent architecture has had a performance with an accuracy rate 95%.