We present photometric redshift estimates for galaxies used in the weak lensing analysis of the Dark Energy Survey Science Verification (DES SV) data. Four model- or machine learning-based photometric redshift methods—annz2, bpz calibrated against BCC-Ufig simulations, skynet, and tpz—are analyzed. For training, calibration, and testing of these methods, we construct a catalogue of spectroscopically confirmed galaxies matched against DES SV data. The performance of the methods is evaluated against the matched spectroscopic catalogue, focusing on metrics relevant for weak lensing analyses, with additional validation against COSMOS photo-
z
’s. From the galaxies in the DES SV shear catalogue, which have mean redshift
0.72
±
0.01
over the range
0.3
<
z
<
1.3
, we construct three tomographic bins with means of
z
=
{
0.45
,
0.67
,
1.00
}
. These bins each have systematic uncertainties
δ
z
≲
0.05
in the mean of the fiducial skynet photo-
z

n
(
z
)
. We propagate the errors in the redshift distributions through to their impact on cosmological parameters estimated with cosmic shear, and find that they cause shifts in the value of
σ
8
of approximately 3%. This shift is within the one sigma statistical errors on
σ
8
for the DES SV shear catalogue. We further study the potential impact of systematic differences on the critical surface density,
Σ
crit
, finding levels of bias safely less than the statistical power of DES SV data. We recommend a final Gaussian prior for the photo-
z
bias in the mean of
n
(
z
)
of width 0.05 for each of the three tomographic bins, and show that this is a sufficient bias model for the corresponding cosmology analysis.