This work focuses on the viability of using a cell-phone as mobile museum guidance. The integrated cell-phone camera is used to recognize the paintings in the gallery. The chosen solution is based on a client-server architecture and the object recognition is based on local features. The study focuses on the comparison, in terms of time and performance, of the Scale-Invariant Feature Transform (SIFT), the Speeded Up Robust Features (SURF), the Nearest Neighbor Search (NNS) match and a k-means trees based search. It was found that SIFT outperforms SURF in terms of performance but is dominated in terms of time. Finally, the combination of SIFT and k-means based search provides a good compromise for the low-resolution images necessary in this setup. The study was performed using a windows mobile operated cell-phone and the 200 test images were taken on site from 4 different perspectives. The reference data set consisted of 1002 different art works of the Louvre.