The continuos time observer design based on differential neural networks (DNN) is presented. It is tested by the application to an immunological interaction model for HIV. To give the corresponding data interpretation and to justify the observability property the Kirschner’s model is used. The suggested observer estimates the model’s variables behavior providing the evolution of the number of infected and not infected lymphocyte T-CD4 cells during 10 days approximately starting from the treatment using Zidovuina (AZT). This approach does not require any model of this process to be in use. The obtained results are shown to allow the programming a high-quality medication protocol for zero-positive patients or AIDS patients.