  The present work investigates the application of Artificial Neural Networks
(ANNs) to estimate the Reynolds ($Re$) number for flows around a cylinder. The
data required to train the ANN was generated with our own implementation of a
Lattice Boltzmann Method (LBM) code performing simulations of a 2-dimensional
flow around a cylinder. As results of the simulations, we obtain the velocity
field ($\vec{v}$) and the vorticity ($\vec{\nabla}\times\vec{v}$) of the fluid
for 120 different values of $Re$ measured at different distances from the
obstacle and use them to teach the ANN to predict the $Re$. The results
predicted by the networks show good accuracy with errors of less than $4\%$ in
all the studied cases. One of the possible applications of this method is the
development of an efficient tool to characterize a blocked flowing pipe.
