Automatic target classification under all conditions is a key challenge for modern radar and sonar systems. Echolocating nectar feeding bats are able to detect and select flowers of bat-pollinated plants even in highly cluttered environments. It is thought that these flowers have evolved to ease classification by bats, and that their echo-acoustic signatures contain critical information that aids the bat in choosing the most suitable flowers. In investigating the features of these flowers that aid the bats search for nectar, the strategy underpinning the task of classification of static targets by bats may be understood and this may additionally offer lessons for radar and sonar systems. Here, we analyse a real set of data containing high range resolution profiles of unpollinated corollas of Cobaea scandens, which is a flower of the type that is pollinated by bats. These were collected by transmitting a synthetic wideband linear chirp with an acoustic radar capable of very high range resolution. Classification performance of a k-NN classifier and a Naive Bayesian classifier is assessed using information available in both the time and frequency domains. This facilitates quantification of the differences in these echoes because of the flower wilting process and lack of physical parts.