  Artificial neural networks bridge input data into output results by
approximately encoding the function that relates them. This is achieved after
training the network with a collection of known inputs and results leading to
an adjustment of the neuron connections and biases. In the context of quantum
detection schemes, neural networks find a natural playground. In particular, in
the presence of a target, a quantum sensor delivers a response, i.e., the input
data, which can be subsequently processed by a neural network that outputs the
target features. We demonstrate that adequately trained neural networks enable
to characterize a target with minimal knowledge of the underlying physical
model, in regimes where the quantum sensor presents complex responses, and
under a significant shot noise due to a reduced number of measurements. We
exemplify the method with a development for $^{171}$Yb$^{+}$ atomic sensors.
However, our protocol is general, thus applicable to arbitrary quantum
detection scenarios.
