In the field of image processing and computer vision face recognition is one of the most studied research domain. It has large variety of applications in different areas like security and surveillance systems, identification and authentication etc. In this paper we propose to analyze the face recognition system based on the eigenface [22] method under different conditions. The eigenface method is a statistical dimensionality reduction method, which obtains the adequate face space, out of a given training database. The idea of observing the performances ie the recognition rate in different situations (like presence or absence of important facial features such as glasses or beard) came from the diploma work [20]. The experiments described in this article study the recognition performance of the algorithm, by varying the number of considered feature vectors. Beside of these, we studied the behavior of such a system if the analyzed individual is wearing glasses or beard. Finally, we concentrate on carrying out experiments for noisy images by adding common types of noise like salt & pepper noise, Gaussian noise or Poisson noise to every test image.