if covariance is -150, what is the type of relationship between two variables? The covariance of of two variables has been calculated to be -150. what does the statistics telling about the relationship between two variables ?
 A: It tells you only that relationship is negative. This means that low values of one variable tend to occur together with high values of the other.
It is hard to tell if this covariance is big or small (if your relationship is strong or weak) because $cov(X,Y)$ ranges from $-sd(X)\cdot sd(Y)$ to $sd(X)\cdot sd(Y)$. So it depends on the scale of your variables.
To judge if this relationship is strong or not, you need to convert covariance to correlation (divide it by $sd(X)\cdot sd(Y)$). This ranges from $-1$ to $1$ and many different guidelines for interpretation can be found in the Web and textbooks.
You can run test for significance of correlation too.
A: To add to Łukasz Deryło's answer: as he writes, a covariance of -150 implies a negative relationship. Whether this is a strong relationship or a weak one depends on the variables' variances. Below I plot examples for a strong relationship (each separate variable has a variance of 200, so the covariance is large, in absolute terms, compared to the variance), and for a weak relationship (each variance is 2000, so the covariance is small, in absolute terms, compared to the variance).
Strong relationship, variance <- 200:

Weak relationship, variance <- 2000:

R code:
library(MASS)

nn <- 100
epsilon <- 0.1
variance <- 2000 # weak relationship

opar <- par(mfrow=c(2,2))
    for ( ii in 1:4 ) {
        while ( TRUE ) {
            dataset <- mvrnorm(n=100,mu=c(0,0),Sigma=rbind(c(2000,-150),c(-150,2000)))
            if ( abs(cov(dataset)[1,2]-(-150)) < epsilon ) break
        }   
        plot(dataset,pch=19,xlab="",ylab="",main=paste("Covariance:",cov(dataset)[1,2]))
    }
par(opar)

EDIT: Anscombe's quartet
As whuber notes, the covariance in itself doesn't really tell us a lot about a dataset. To illustrate, I'll take Anscombe's quartet and modify it slightly. Note how very different scatterplots can all have the same (rounded) covariance of -150:

anscombe.mod <- anscombe
anscombe.mod[,c("x1","x2","x3","x4")] <- sqrt(150/5.5)*anscombe[,c("x1","x2","x3","x4")]
anscombe.mod[,c("y1","y2","y3","y4")] <- -sqrt(150/5.5)*anscombe[,c("y1","y2","y3","y4")]
opar <- par(mfrow=c(2,2))
    with(anscombe.mod,plot(x1,y1,pch=19,main=paste("Covariance:",round(cov(x1,y1),0))))
    with(anscombe.mod,plot(x2,y2,pch=19,main=paste("Covariance:",round(cov(x2,y2),0))))
    with(anscombe.mod,plot(x3,y3,pch=19,main=paste("Covariance:",round(cov(x3,y3),0))))
    with(anscombe.mod,plot(x4,y4,pch=19,main=paste("Covariance:",round(cov(x4,y4),0))))
par(opar)

FINAL EDIT (I promise!)
Finally, here is a covariance of -150 with perhaps the most tenuous "negative relationship" between $x$ and $y$ imaginable:

xx <- yy <- seq(0,100,by=10)
yy[9] <- -336.7
plot(xx,yy,pch=19,main=paste("Covariance:",cov(xx,yy)))

