  Machine learning techniques have been increasingly used in astronomical
applications and have proven to successfully classify objects in image data
with high accuracy. The current work uses archival data from the Faint Images
of the Radio Sky at Twenty Centimeters (FIRST) to classify radio galaxies into
four classes: Fanaroff-Riley Class I (FRI), Fanaroff-Riley Class II (FRII),
Bent-Tailed (BENT), and Compact (COMPT). The model presented in this work is
based on Convolutional Neural Networks (CNNs). The proposed architecture
comprises three parallel blocks of convolutional layers combined and processed
for final classification by two feed-forward layers. Our model classified
selected classes of radio galaxy sources on an independent testing subset with
an average of 96\% for precision, recall, and F1 score. The best selected
augmentation techniques were rotations, horizontal or vertical flips, and
increase of brightness. Shifts, zoom and decrease of brightness worsened the
performance of the model. The current results show that model developed in this
work is able to identify different morphological classes of radio galaxies with
a high efficiency and performance
