With advances in sensor technology, the availability of multispectral cameras and their use are increasing. Having more information compared to a three-channel camera has its advantages but the data must be handled appropriately. In this work, we are interested in multispectral camera characterization. We measure the camera characterization performance by two methods, by linear mapping and through spectral reconstruction. Linear mapping is used in 3-channel camera characterization and we use the same method for a multispectral camera. We also investigate whether instead of linear mapping, spectral reconstruction from the camera data improves the performance of color reproduction. The recovery of reflectance spectra is an under-determined problem and certain assumptions are required for obtaining a unique solution. Linear methods are generally used for spectral reconstruction from the camera data and are based on training on known spectra. These methods can perform well when the test data consists of a subset of the training spectra, however, their performance is significantly reduced when the test data is different. In this paper, we also investigate the role of training spectra for camera characterization. Five different spectral reflectance datasets are used for training the camera characterization models. Finally we provide a comparison between the linear mapping and spectral reconstruction methods for multispectral camera characterization and also test the camera characterization framework on hyperspectral images of natural scenes.