  We explore the prospects of predicting emission line features present in
galaxy spectra given broad-band photometry alone. There is a general consent
that colours, and spectral features, most notably the 4000 A break, can predict
many properties of galaxies, including star formation rates and hence they
could infer some of the line properties. We argue that these techniques have
great prospects in helping us understand line emission in extragalactic objects
and might speed up future galaxy redshift surveys if they are to target
emission line objects only. We use two independent methods, Artifical Neural
Neworks (based on the ANNz code) and Locally Weighted Regression (LWR), to
retrieve correlations present in the colour N-dimensional space and to predict
the equivalent widths present in the corresponding spectra. We also investigate
how well it is possible to separate galaxies with and without lines from broad
band photometry only. We find, unsurprisingly, that recombination lines can be
well predicted by galaxy colours. However, among collisional lines some can and
some cannot be predicted well from galaxy colours alone, without any further
redshift information. We also use our techniques to estimate how much
information contained in spectral diagnostic diagrams can be recovered from
broad-band photometry alone. We find that it is possible to classify AGN and
star formation objects relatively well using colours only. We suggest that this
technique could be used to considerably improve redshift surveys such as the
upcoming FMOS survey and the planned WFMOS survey.
