Unlike the top-down photolithographic CMOS VLSI process, cost-effective bulk fabrication of nano devices calls for a bottom-up approach, generally called self-assembly. Self-assembly, however, inherently lends itself to innate disparities in the structure of nominally identical nanodevices and, consequently, wide inter-device variance in their functionality. As a result, nanodevice characterization and testing calls for a slow and tedious procedure involving a large number of measurements. In this work, we discuss a statistical approach which learns measurement correlations from a small set of fully characterized nanodevices and utilizes the extracted knowledge to simplify the process for the rest of thenanodevices. More specifically, we employ various machine-learning methods which rely on a small subset of measurements to i) predict the performances of a fabricated nanodevice, ii)decide whether a nanodevice passes or fails a given set of specifications, andiii) bin a nanodevice with regards to several sets of increasingly strict specifications. The proposed methods are demonstrated and their effectiveness is assessed, within the context of nanowire-based chemical sensing, using a set of fabricated and fully characterized nanowires.