The use of local image features (LIF) for object class recognition is becoming increasingly popular. To better understand the suitability and power of existing LIFs for object class recognition, a simple but useful method is proposed in evaluation of such features. We have compared the performance of eight frequently used LIFs by the proposed method on two popular databases. We have used F-measure criterion for this evaluation. It is found that the individual performance of SURF and SIFT features are better than that of the global features on ETH-80 database with considerably lower number of training objects. However, it may not be good enough for more challenging object class recognition problem (e.g. Caltech-101). The evaluation of LIFs suggests the requirement for further investigation of more complementary LIFs.