In this paper, we propose a conditional information analysis in which information on important patterns is selectively detected. The selection is realized by ?-information, which can be used to maximize or minimize selectively conditional information, according to the important, or characteristics of input patterns. The information analysis was applied to two feature detection problems: an alphabet character recognition and medical data. In both problems, experimental results confirmed that conditional information is flexibly maximized or minimized, depending upon input patterns. We could also see that conditional information is a good measure to distinguish between different classes.