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

What makes slow samples slow in the Sherrington-Kirkpatrick model