Containing enormous amounts of various types of data, web has become the main source for finding
the desired information. Meanwhile retrieving the desired information in such a vast heterogeneous
environment is much difficult. This situation has led to a drastic increase in the popularity of internet search
engines. Undoubtedly, designing both efficient and effective ranking strategies as the basic core of web
information retrieval systems are unavoidable. Unfortunately most of the proposed ranking algorithms do not
work very well over general datasets because of their fixed configurations. Many of these algorithms also suffer
from their computational costs. Regarding these shortcoming, in this paper, a new ranking framework named
"NNRank" is proposed which uses the primitive features of web documents from the categories of content and
context using an artificial neural network. The neural networks selected in our approach is a radial basis
function or a principle component analysis neural network which due to their high convergence rate, have the
capability to exhibit a high performance with a limited number of features. Experimental results based on TREC
2004 gathered in Microsoft LETOR dataset, indicate a noticeable enhancement comparing to the well-known
ranking algorithms such as TF-IDF, PageRank and HITS. The results are also comparable with those of BM25.