  Using results of a Monte Carlo simulation of the Sherrington-Kirkpatrick
model, we try to characterize the slow disorder samples, namely we analyze
visually the correlation between the relaxation time for a given disorder
sample $J$ with several observables of the system for the same disorder sample.
For temperatures below $T_c$ but not too low, fast samples (small relaxation
times) are clearly correlated with a small value of the largest eigenvalue of
the coupling matrix, a large value of the site averaged local field probability
distribution at the origin, or a small value of the squared overlap $<q^2>$.
Within our limited data, the correlation remains as the system size increases
but becomes less clear as the temperature is decreased (the correlation with
$<q^2>$ is more robust) . There is a strong correlation between the values of
the relaxation time for two distinct values of the temperature, but this
correlation decreases as the system size is increased. This may indicate the
onset of temperature chaos.
