  Context. Computational techniques are essential for mining large databases
produced in modern surveys with value-added products. Aims. This paper presents
a machine learning procedure to carry out simultaneously galaxy morphological
classification and photometric redshift estimates. Currently, only spectral
energy distribution (SED) fitting has been used to obtain these results all at
once. Methods. We used the ancillary data gathered in the OTELO catalog and
designed a non-sequential neural network that accepts optical and near-infrared
photometry as input. The network transfers the results of the morphological
classification task to the redshift fitting process to ensure consistency
between both procedures. Results. The results successfully recover the
morphological classification and the redshifts of the test sample, reducing
catastrophic redshift outliers produced by SED fitting and avoiding possible
discrepancies between independent classification and redshift estimates. Our
technique may be adapted to include galaxy images to improve the
classification.
