We present LCAV-31, a multi-view object recognition dataset designed specifically for benchmarking light field image analysis tasks. The principal distinctive factor of LCAV-31 compared to similar datasets is its design goals and availability of novel visual information for more accurate recognition (i.e. light field information). The dataset is composed of 31 object categories captured from ordinary household objects. We captured the color and light field images using the recently popularized Lytro consumer camera. Different views of each object have been provided as well as various poses and illumination conditions. We explain all the details of different capture parameters and acquisition procedure so that one can easily study the effect of different factors on the performance of algorithms executed on LCAV-31. Moreover, we apply a set of basic object recognition algorithms on LCAV-31. The results of these experiments can be used as a baseline for further development of novel algorithms.