A standalone radiation detection and identification system is designed and tested which quantizes gamma ray energies with a scintillator, photomultiplier tube, and a custom multichannel analyzer chip to construct a gamma ray energy histogram. The histogram is used as the input to a fast, low memory, versatile neural network that runs in software on a microcontroller and identifies in real time which radioisotopes are present in the radiation source. The neural network accurately identifies the radioisotopes for which it has been trained, running in under 91.4 ms, consuming less than 6.2 kB of memory, and expending 274 μJ of energy each time it is executed.